Sparse superposition encoder and decoder for communications system

ABSTRACT

A computationally feasible encoding and decoding arrangement and method for transmission of data over an additive white Gaussian noise channel with average codeword power constraint employs sparse superposition codes. The code words are linear combinations of subsets of vectors from a given dictionary, with the possible messages indexed by the choice of subset. An adaptive successive decoder is shown to be reliable with error probability exponentially small for all rates below the Shannon capacity.

RELATIONSHIP TO OTHER APPLICATION

This application claims the benefit of the filing date of U.S. Provisional Patent Application Ser. No. 61/332,407 filed May 7, 2010, Conf. No. 1102 (Foreign Filing License Granted) in the names of the same inventors as herein. The disclosure in the identified United States Provisional Patent Application is incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to data transmission systems, and more particularly, to a system of encoding, transmitting, and decoding data that is fast and reliable, and transmits data at rates near theoretical capacity along a noisy transmission medium.

2. Description of the Prior Art

Historically, in the analog communication era the FCC would allocate a predetermined bandwidth, i.e., frequency band, for transmission of information, illustratively music, over the air as the transmission medium. The signal typically took the form of a sinusoidal carrier wave that was modulated in response to the information. The modulation generally constituted analog modulation, where the amplitude of the carrier signal (AM) was varied in response to the information, or alternatively the frequency of the carrier signal was varied (FM) in response to the information desired to be transmitted. At the receiving end of the transmission, a receiver, typically a radio, consisting primarily of a demodulator, would produce a signal responsive to the amplitude or frequency of the modulated carrier, and eliminate the carrier itself. The received signal was intended to replicate the original information, yet was subjected to the noise of the transmission channel.

During the 1940s, a mathematical analysis was presented that shifted the thinking as to the manner by which information could reliably be transferred. Mathematician Claude Shannon introduced the communications model for coded information in which an encoder is introduced before any modulation and transmission and at the receiver a decoder is introduced after any demodulation. Shannon proved that rather than listening to noisy communication, the decoding end of the transmission could essentially recover the originally intended information, as if the noise were removed, even though the transmitted signal could not easily be distinguished in the presence of the noise. One example of this proof is in modern compact disc players where the music heard is essentially free of noise notwithstanding that the compact disc medium might have scratches or other defects.

In regard of the foregoing, Shannon identified two of the three significant elements associated with reliable communications. The first concerned the probability of error, whereby in an event of sufficient noise corruption, the information cannot be reproduced at the decoder. This established a need for a system wherein as code length increases, the probability of error decreases, preferably exponentially.

The second significant element of noise removal related to the rate of the communication, which is the ratio of the length of the original information to the length in its coded form. Shannon proved mathematically that there is a maximum rate of transmission for any given transmission medium, called the channel capacity C.

The standard model for noise is Gaussian. In the case of Gaussian noise, the maximum rate of the data channel between the encoder and the decoder as determined by Shannon corresponds to the relationship C=(½)log₂(1+P/σ²), where P/σ² corresponds to the signal-to-noise ratio (i.e., the ratio of the signal power to the noise power, where power is the average energy per transmitted symbol). Here the value P corresponds to a power constraint. More specifically, there is a constraint on the amount of energy that would be used during transmission of the information.

The third significant element associated with reliable communications is the code complexity, comprising encoder and decoder size (e.g., size of working memory and processors), the encoder and decoder computation time, and the time delay between sequences of source information and decoded information.

To date, no one other than the inventors herein has achieved a computationally feasible, mathematically proven scheme that achieves rates of transmission that are arbitrarily close to the Shannon capacity, while also achieving exponentially small error probability, for an additive noise channel.

Low density parity check (LDPC) codes and so called “turbo” codes were empirically demonstrated (in the 1990s) through simulations to achieve high rate and small error probability for a range of code sizes, that make these ubiquitous for current coded communications devices, but these codes lack demonstration of performance scalability for larger code sizes. It has not been proven mathematically that for rate near capacity, that these codes will achieve the low probabilities of error that will in the future be required of communications systems. An exception is the case of the comparatively simple erasure channel, which is not an additive noise channel.

Polarization codes, a more recent development of the last three years of a class of computationally feasible codes for channels with a finite input set, do achieve any rate below capacity, but the scaling of the error probability is not as effective.

There is therefore a need for a code system for communications that can be mathematically proven as well as empirically demonstrated to achieve the necessary low probabilities of error, high rate, and feasible complexity, for real-valued additive noise channels.

There is additionally a need for a code system that can mathematically be proven to be scalable wherein the probability of error decreases exponentially as the code word length is increased, with an acceptable scaling of complexity, and at rates of transmission that approach the Shannon capacity.

SUMMARY OF THE INVENTION

The foregoing and other deficiencies in the prior art are solved by this invention, which provides, in accordance with a first apparatus aspect thereof, sparse superposition encoder for a structured code for encoding digital information for transmission over a data channel. In accordance with the invention, there is provided a memory for storing a design matrix (also called the dictionary) formed of a plurality of column vectors X₁, X₂, . . . , X_(N), each such vector having n coordinates. An input is provided for entering a sequence of input bits u₁, u₂, . . . , u_(K) which determine a plurality of coefficients β₁, . . . , β_(N). Each of the coefficients is associated with a respective one of the vectors of the design matrix to form codeword vectors, with selectable real or complex-valued entries. The entries take the form of superpositions β₁X₁+β₂X₂+ . . . +β_(N)X_(N). The sequence of bits u₁, u₂, . . . , u_(K) constitute in this embodiment of the invention at least a portion of the digital information.

In one embodiment, the plurality of the coefficients β_(j) have selectably a determined non-zero value, or a zero value. In further embodiments, at least some of the plurality of the coefficients β_(j) have a predetermined value multiplied selectably by +1, or the predetermined value multiplied by −1. In still further embodiments of the invention, at least some of the plurality of the coefficients β_(j) have a zero value, the number non-zero being denoted L and the value B=N/L controlling the extent of sparsity, as will be further described in detail herein.

In a specific illustrative embodiment of the invention, the design matrix that is stored in the memory is partitioned into L sections, each such section having B columns, where L>1. In a further aspect of this embodiment, each of the L sections of size B has B memory positions, one for each column of the dictionary, where B has a value corresponding to a power of 2. The positions are addressed (i.e., selected) by binary strings of length log₂(B). The input bit string of length K=L log₂ B is, in some embodiments, split into L substrings, wherein for each section the associated substring provides the memory address of which one column is flagged to have a non-zero coefficient. In an advantageous embodiment, only 1 out of the B coefficients in each section is non-zero.

In a further embodiment of the invention, the L sections each has allocated a respective power that determines the squared magnitudes of the non-zero coefficients, denoted P₁, P₂, . . . , P_(L), i.e., one from each section. In a further embodiment, the respectively allocated powers sum to a total P to achieve a predetermined transmission power. In still further embodiments, the allocated powers are determined in a set of variable power assignments that permit a code rate up to value C_(B) where, with increasing sparsity B, this value approaches the capacity C=½ log₂(1+P/σ²) for the Gaussian noise channel of noise variance σ².

In one embodiment of the invention, the code rate is R=K/n, for an arbitrary R where R<C, for an additive channel of capacity C. In such a case, the partitioned superposition code rate is R=(L log B)/n.

In another embodiment of the invention, there is provided an adder that computes each entry of the codeword as the superposition of the corresponding dictionary elements for which the coefficients are non-zero.

In a still further embodiment of the invention, there are provided n adders for computing the codeword entries as the superposition of selected L columns of the dictionary in parallel. In an advantageous embodiment, before initiating communications, the specified magnitudes are pre-multiplied to the columns of each section of the design matrix X, so that only adders are subsequently required of the encoder processor to form the code-words. Further in accordance with the embodiment of the invention, R/log(B) is arranged to be bounded so that encoder computation time to form the superposition of L columns is not larger than order n, thereby yielding constant computation time per symbol sent.

In another embodiment of the invention, the encoder size complexity is not more than the nBL memory positions to hold the design matrix and the n adders. Moreover, in some embodiments, the value of B is chosen to be not more than a constant multiplied by n, whereupon also L is not more than n divided by a log. In this manner, the encoder size complexity nBL is not more than n³. Also R/log(B) is, in some embodiments, chosen to be small.

In yet another embodiment of the invention, the input to the code arises as the output of a Reed-Solomon outer code of alphabet size B and length L. This serves to maintain an optimal separation, the distance being measured by the fraction of distinct selections of non-zero terms.

In accordance with an advantageous embodiment of the invention, the dictionary is generated by independent standard normal random variables. Preferably, the random variables are provided to a specified predetermined precision. In accordance with some aspects of this embodiment of the invention, the dictionary is generated by independent, equiprobable, +1 or −1, random variables.

In accordance with a second apparatus aspect of the invention, there is provided an adaptive successive decoder for a structured code whereby digital information that has been received over a transmission channel is decoded. There is provided in this embodiment of the invention a memory for storing a design matrix (dictionary) formed of a plurality of vectors X₁, X₂, . . . X_(N), each such vector having n coordinates. An input receives the digital information Y from the transmission channel, the digital information having been encoded as a plurality of coefficients β₁ . . . β_(N), each of the coefficients being associated with a respective one of the vectors of the design matrix to form codeword vectors in the form of superpositions β₁X₁+β₂X₂+ . . . +β_(N)X_(N). The superpositions have been distorted during transmission to the form Y. In addition, there is provided a first inner product processor for computing inner products of Y with each of the plurality of vectors X₁, X₂, . . . X_(N), stored in the memory to determine which of the inner products has a value above a predetermined threshold value.

In one embodiment of this second apparatus aspect of the invention the first inner product processor performs a plurality of first inner products in parallel.

As a result of data transmission over a noisy channel, the resulting distortion of the superpositions is responsive to an additive noise vector ξ, having a distribution N(0,σ²I).

In a further embodiment of the invention, there is additionally provided a processor of adders for superimposing the columns of the design matrix that have inner product values above the predetermined threshold value. The columns are flagged and the superposition of flagged columns are termed the “fit.” In the practice of this second aspect of the invention, the fit is subtracted from Y leaving a residual vector r. A further inner product processor, in some embodiments, receives the residual vector r and computes selected inner products of the residual r with each of the plurality of vectors X₁, X₂, . . . X_(N), not previously flagged, for determining which of these columns to flag as having therein an inner product value above the predetermined threshold value.

In an advantageous embodiment of the invention, upon receipt of the residual vector r by the further inner product processor, a new Yvector is entered into the input so as to for achieve simultaneous pipelined inner product processing.

In a further embodiment of the invention, there are additionally provided k−2 further inner product processors arranged to operate sequentially with one another and with the first and further inner product processors. The additional k−2 inner product processors compute selected inner products of respectively associated ones of residuals with each of the plurality of vectors X₁, X₂, . . . X_(N), not previously flagged, to determine which of these columns to flag as having there an inner product value above the predetermined threshold value. In accordance with some embodiments, the k inner product processors are configured as a computational pipeline to perform simultaneously their respective inner products of residuals associated with a sequence of corresponding received Y vectors.

In a still further embodiment, in each of the inner product processors there are further provided N accumulators, each associated with a respective column of the design matrix memory to enable parallel processes. In another embodiment, each of the inner product processors is further provided with Nmultipliers, each of which is associated with a respective column of the design matrix, for effecting parallel processing.

In yet another embodiment, there are further provided a plurality of comparators for comparing respective ones of the inner products with a predetermined threshold value. The plurality of comparators, in some embodiments, store a flag responsive to the comparison of the inner products with the predetermined threshold value.

In accordance with a specific illustrative embodiment of the invention, there is provided a processor for computing superposition of fit components from the flagged columns of the design matrix. In a further embodiment, there are provided k−1 inner product processors, each of which computes, in succession, the inner product of each column that has not previously been flagged, with a linear combination of Y and of previous fit components.

In a still further embodiment of the invention, there is provided a processor by which the received Y and the fit components are successively orthogonalized.

The linear combination of Y and of previous fit components is, in one embodiment, formed with weights responsive to observed fractions of previously flagged terms. In a further embodiment, the linear combination of Yand of previous fit components is formed with weights responsive to expected fractions of previously flagged terms. In a highly advantageous embodiment of the invention, the expected fractions of flagged terms are determined by an update function processor.

The expected fractions of flagged terms are pre-computed, in some embodiments, before communication, and are stored in a memory of the decoder for its use.

The update function processor determines g_(L)(x) which evaluates the conditional expected total fraction of flagged terms on a step if the fraction on the previous step is x. The fraction of flagged terms is, in some embodiments of the invention, weighted by the power allocation divided by the total power. In further embodiments of the invention, the update function processor determines g_(L)(x) as a linear combination of probabilities of events of inner product above threshold.

In accordance with a further embodiment of the invention, the dictionary is partitioned whereby each column has a distinct memory position and the sequence of binary addresses of flagged memory positions forms the decoded bit string. In one embodiment, the occurrence of more than one flagged memory position in a section or no flagged memory positions in a section is denoted as an erasure, and one incorrect flagged position in a section is denoted as an error. In a still further embodiment, the output is provided to an outer Reed-Solomon code that completes the correction of any remaining small fraction of section mistakes.

In accordance with a third apparatus aspect of the invention, there is provided a performance-scalable structured code system for transferring data over a data channel having code rate capacity of C. The system is provided with an encoder for specified system parameters of input-length K and code-length n. The encoder is provided with a memory for storing a design matrix formed of a plurality of vectors X₁, X₂, . . . X_(N), each such vector having n coordinates. Additionally, the encoder has an input for entering a plurality of bits bits u₁, u₂, . . . , u_(K) that determine a plurality of coefficients β₁, . . . , β_(N). Each of the coefficients is associated with a respective one of the vectors of the design matrix to form codeword vectors in the form of superpositions β₁X₁+β₂X₂+ . . . +β_(N)X_(N). There is additionally provided on the encoder an output for delivering the codeword vectors to the transmission channel. In addition to the foregoing, there is provided a decoder having an input for receiving the superpositions β₁X₁+β₂X₂+ . . . +β_(N)X_(N), the superpositions having been distorted during transmission to the form Y. The decoder is further provided with a first inner product processor for computing inner products of Y with each of the plurality of vectors X₁, X₂, . . . , X_(N), stored in the memory to determine which of the inner products has a value above a predetermined threshold value.

In one embodiment of this third apparatus aspect of the invention, the encoder and decoder adapt a choice of system parameters to produce a smallest error probability for a specified code rate and an available code complexity.

In a further embodiment, in response to the code-length n, the choice of system parameters has exponentially small error probability and a code complexity scale not more than n³ for any code rate R less than the capacity C for the Gaussian additive noise channel.

In an advantageous embodiment of the invention, there is provided a performance-scale processor for setting of systems parameters K and n. The performance-scale processor is responsive to specification of the power available to the encoder of the channel. In other embodiments, the performance-scale processor is responsive to specification of noise characteristics of the channel. In still further embodiments, the performance-scale processor is responsive to a target error probability. In yet other embodiments, the performance-scale processor is responsive to specification of a target decoder complexity. In still other embodiments, the performance-scale processor is responsive to specification of a rate R, being any specified value less than C.

In some embodiments of the invention the performance-scale processor sets values of L and B for partitioning of the design matrix. In respective other embodiments, the performance-scale processor:

-   -   sets values of power allocation to each non-zero coefficient;     -   sets values of threshold of inner product test statistics; and     -   sets a value of the maximum number of steps of the decoder.

In an advantageous embodiment, the decoder includes multiple inner product processors operating in succession to provide steps of selection of columns of the fit. The performance-scale processor performs successive evaluations of an update function g_(L)(x) specifying an expected total fraction of correctly flagged terms on a step if the total fraction on the previous step were equal to the value x.

In further embodiments, the fraction of flagged terms is weighted by the power allocation divided by the total power. The update function processor in some embodiments, determines g_(L)(x) as a linear combination of probabilities determined for events of an inner product above threshold. The system parameters are, in some embodiments, optimized by examination of a sequence of system parameters and performing successive evaluations of the update function for each.

In the practice of some embodiments of the invention, the encoder is a sparse superposition encoder. The decoder is in some embodiments an adaptive successive decoder. An outer Reed-Solomon encoder is, in some embodiments, matched to the inner sparse superposition encoder. In other embodiments, the outer Reed-Solomon decoder is matched to the adaptive successive decoder.

In a specific illustrative embodiment of the invention, the encoder is a sparse partitioned superposition encoder, and the decoder is an adaptive successive decoder, with parameter values set at those demonstrated to produce exponentially small probability of more than a small specified fraction of mistakes, with exponent responsive to C_(B)−R. The decoder has a hardware implemented size-complexity not more than a constant times n² B, a constant time-complexity rate, a delay not more than a constant times log(B), and a code rate R up to a value C_(B) of quantified approach to the Channel Capacity C of the Gaussian noise channel as the values of n and B are scaled to account for increasing density and the extent of computer memory and processors.

Further in accordance with this embodiment, the inner-code sparse superposition encoder and decoder having the characteristics herein above set forth are matched to an outer Reed-Solomon encoder and decoder, respectively, whereby mistakes are corrected, except in an event of an exponentially small probability.

In accordance with a method aspect of the invention there is provided a method of decoding data, the method including the steps of:

computing an inner products of a received signal Ywith each column of a Xstored in a design matrix;

identifying ones of the inner products that exceed a predetermined threshold value;

forming an initial fit fit₁; and, then in succession for k>1,

computing inner products of residuals Y−fit_(k−1), with each column of X;

identifying columns for which the inner product exceeds a predetermined threshold value;

adding those columns for which the inner product exceeds the predetermined threshold value to the fit fit_(k−1); and

selectably terminating computing of inner products of residuals when k is a specific multiple of log B or when no inner products of residuals exceed a predetermined threshold value.

In one embodiment of this method aspect of the invention, there are provided the further steps of:

subtracting a sum of the identified products that exceed a predetermined threshold value from Yto form a residual r; and

computing an inner products of the residual r with each column of aXstored in the design matrix.

In a further embodiment, there are further provided the steps of:

repeating the steps of subtracting a sum of the identified products that exceed a predetermined threshold value from Yto form a residual r and computing an inner products of the residual r with each column of a X stored in the design matrix; and

selectably terminating computing of inner products of residuals when k is a specific multiple of log B or when no inner products of residuals exceed a predetermined threshold value.

BRIEF DESCRIPTION OF THE DRAWING

Comprehension of the invention is facilitated by reading the following detailed description, in conjunction with the annexed drawing, in which:

FIG. 1 is a simplified schematic representation of an encoder constructed in accordance with the principles of the invention arranged to deliver data encoded as coefficients multiplied by values stored in a design matrix to a data channel having a predetermined maximum transmission capacity, the encoded data being delivered to a decoder that contains a local copy of a design matrix and a coefficient extraction processor, the decoder being constructed in accordance with the principles of the invention;

FIG. 2 is a simplified schematic representation of an encoder constructed in accordance with the principles of the invention arranged to deliver data encoded as coefficients multiplied by values stored in a design matrix to a data channel having a predetermined maximum transmission capacity, the encoded data being delivered to a decoder that contains a local copy of a design matrix and an adaptive successive decoding processor, the decoder being constructed in accordance with the principles of the invention;

FIG. 3 is a simplified schematic representation of an encoder constructed in accordance with the principles of the invention arranged to deliver data encoded as coefficients multiplied by values stored in a design matrix to a data channel having a predetermined maximum transmission capacity, the encoded data being delivered to a decoder that contains a plurality of local copies of a design matrix, each associated with a sequential pipelined decoding processor constructed in accordance with the principles of the invention that achieves continuous data decoding;

FIG. 4 is a graphical representation of a plot of the function g_(L)(x), wherein the dots acy indicate the sequence q_(1,k) ^(adj) for the 16 steps. B=2¹⁶, snr=7, R=0.74 and L is taken to be equal to B (Note: snr→“signal-to-noise ratio”). The height reached by the g_(L)(x) curve at the final step corresponds to a weighted correct detection rate target of 0.993, un-weighted 0.986, for a failed detection rate target of 0.014. The accumulated false alarm rate bound is 0.008. The probability of mistake rates larger than these targets is bounded by 4.8×10⁻⁴;

FIG. 5 is a graphical representation of a progression of a specific illustrative embodiment of the invention, wherein snr=15. The weighted (unweighted) detection rate is 0.995 (0.983) for a failed detection rate of 0.017 and the false alarm rate is 0.006. The probability of mistakes larger than these targets is bounded by 5.4×10⁻⁴;

FIG. 6 is a graphical representation of a progression of a specific illustrative embodiment of the invention, wherein snr=1. The detection rate (both weighted and un-weighted) is 0.944 and the false alarm and failed detection rates are 0.016 and 0.056 respectively, with the corresponding error probability bounded by 2.1×10⁻⁴;

FIG. 7 is a graphical representation of a plot of an achievable rate as a function of B for snr=15. Section error rate is controlled to be between 9 and 10%. For the curve using simulation runs the rates are exhibited for which the empirical probability of making more than 10% section mistakes is near 10⁻³;

FIG. 8 is a graphical representation of a plot of an achievable rate as a function of B for snr=7. Section error rate is controlled to be between 9 and 10%. For the curve using simulation runs the rates are exhibited for which the empirical probability of making more than 10% section mistakes is near 10⁻³; and

FIG. 9 is a graphical representation of a plot of an achievable rate as a function of B for snr=1. Section error rate is controlled to be between 9 and 10%. For the curve using simulation runs the rates are exhibited for which the empirical probability of making more than 10% section mistakes is near 10⁻³.

DETAILED DESCRIPTION Glossary of Terms

Adaptive Successive Decoder: An iterative decoder for a partitioned superposition code with hardware design in which an overlapping sequence of sections is tested each step to see which have suitable test statistics above threshold, and flags these to correspond with decoded message segments. The invention here-in determines that an adaptive successive decoder is mistake rate scalable for all code rates R<C_(B) for the Gaussian channel, with size complexity n²B, delay snr log B, constant time complexity rate, and a specified sequence C_(B) that approach the capacity C with increasing B. Thereby when composed with a suitable outer code it is performance scalable with exponentially small error probability and specified control of complexity. [See also: Partitioned Superposition Code, Fixed Successive Decoder, Code-Rate, Complexity, Mistake Rate Scalability, Performance Scalability.]

Additive Noise Channel: A channel specification of the form Y=c+ξ where c is the codeword, ξ is the noise vector, and Y is the vector of n received values. Such a discrete-time model arises from consideration of several steps of the communication system (modulator, transmitter, transmission channel, demodulator and filter) as one code channel for the purpose of focus on issues of coding. It is called a White Additive Noise Channel if the successive values of the resulting noise vector are uncorrelated (wherein the purpose of the filter, a so-called whitening filter, is to produce a successive removal of any such correlation in the original noise of the transmission channel). A channel in which the transmitted signal is attenuated (faded) by a determined amount is converted to an additive noise channel by a rescaling of the magnitude of the received sequence.

Block Error (also called “error”): The event that the decoded message of length K is not equal to the encoded message string. Associate with it is the block error probability.

Channel Capacity C (also called Shannon Capacity): For any channel it is the supremum (least upper bound) of code rates R at which the message is decoded, where for any positive error probability there is a sufficiently complex encoder/decoder pair that has code rate R and error probability as specified. By Shannon theory it is explicitly expressed via specification of signal power and channel characteristics. [See, also “Channel Code System,” “Code Rate,” and “Error Probability”]

Channel Code System: A distillation of the ingredients of the parts of a communication system that focus on the operation and performance of the pathway in succession starting with the message bit sequence, including the channel encoder, the channel mapping the sequence of values to be sent to the values received at each recipient, the channel decoder (one for each recipient), and the decoded message bit sequence (one at each recipient), the purpose of which is to avoid the mistakes that would result from transmission without coding, and the cost of which will be expressed in coding complexity and in limitations on code rate [See also: “Communication System”].

Channel Decoder (also called the “decoder”): A device for converting n received values (the received vector Y) into a string of K bits, the decoded message.

Channel Encoder (also called the “encoder”): A device for converting a K bit message into a string of n values (the codeword) to be sent across a channel, the values being of a form (binary or real or complex) as is suited to the use of the channel. The value n is called the code-length or block-length of the code.

Code Complexity (also called “computational complexity”): Encompasses size complexity, time complexity, and delay. Specific to a hardware instantiation, the size complexity is the sum of the number of fixed memory locations, the number of additional memory locations for workspace of the decoding algorithm, and the number of elementary processors (e.g. multiplier/accumulators, adders, comparators, memory address selectors) that may act in parallel in encoding and decoding operation. With reference to the decoder, the time complexity (or time complexity rate) is the number of elementary operations performed per pipelined received string Y divided by the length of the string n. Time complexity of the encoder is defined analogously. With reference to the decoder, the delay is defined as the count of the number of indices between the flow of received strings Y and the flow of decoded strings concurrently produced.

Code Rate: The ratio R=K/n of the number of message bits to the number of values to be sent across a channel.

Communication System: A system for electronic transfer of source data (voice, music, data bases, financial data, images, movies, text, computer files for radio, telephone, television, internet) through a medium (wires, cables, or electromagnetic radiation) to a destination, consisting of the sequence of actions of the following devices: a source compression (source encoder) providing a binary sequence rendering of the source (called the message bit sequence); a channel encoder providing a sequence of values for transmission, a modulator, a transmitter, a transmission channel, and, for each recipient, a receiver, a filter, a demodulator, a channel decoder, and a source decoder, the outcome of which is a desirably precise rendering of the original source data by each recipient.

Computational Complexity: See, “Code Complexity.”

Decoder: (See, “Channel Decoder”)

Design Matrix (or “Dictionary”): An n by N matrix X of values known to the encoder and decoder. In the context of coding these matrices arise by strategies called superposition codes in which the codewords are linear combinations of the columns of X, in which case power requirements of the code are maintained by restrictions on the scale of the norms of these columns and their linear combinations.

Dictionary: See, “Design Matrix.”

Encoder Term Specification: Each partitioned message segment of length log(B) gives the memory position at which it is flagged that a column is selected (equivalently it is a flagged entry indicating a non-zero coefficient).

(Fixed) Successive Decoder: An iterative decoder for a partitioned superposition code in which it is pre-specified what sequence of sections is to be decoded. Fixed successive decoders that have exponentially small error probability at rates up to capacity unfortunately have exponentially large complexity.

Fraction of Section Mistakes (also called “section error rate”): The proportion of the sections of a message string that are in error. For partitioned superposition codes with adaptive successive decoder, by the invention herein, the mistake rate is likely not more than a target mistake rate which is an expression inversely proportional to s=log₂(B).

Gaussian Channel: An additive noise channel in which the distribution of the noise is a mean zero normal (Gaussian) with the specified variance σ². Assumed to be whitened, it is an Additive White Gaussian Noise Channel (AWGN). The Shannon Capacity of this channel is C=½ log₂(1+P/σ²).

Iterative Decoder: In the context of superposition codes an iterative decoder is one in which there are multiple steps, where for each step there is a processor that takes the results of previous steps and updates the determination of terms of the linear combination that provided the codeword.

Message Sections: A partition of a length K message string into L sections of length s each of which conveys a choice of B=2^(s), with which K=Ls. Typical values of are between about 8 and about 20, though no restriction is made. Also typical is forB and L to be comparable in value, and so to is the codelength n, via the rate relationship nR=K=L log₂(B). That is, the number L of sections matches the codelength n to within a logarithmic factor.

Message Strings: The result of breaking a potentially unending sequence of bits into successive blocks of length denoted as K bits, called the sequence of messages. Typical values of K range from several hundred to several tens of thousands, though no restriction is made.

Noise Variance σ²: The expected squared magnitude of the components of the noise vector in an Additive White Noise Channel.

Outer Code for Inner Code Mistake Correction: A code system in which an outer code such as a Reed-Solomon code is concatenated (composed) with another code (which then becomes the inner code) for the purpose of correction of the small fraction of mistakes made by the inner code. Such a composition converts a code with specified mistake-rate scaling into a code with exponentially small error probability, with slight overall code rate reduction by an amount determined by the target mistake rate [See, also “Fraction of Section Mistakes,” and “Scaling of Section Mistakes”].

Performance Scalable Codes: A performance scalable code system (as first achieved for additive noise channels by the invention here-in) is a structured code system in which for any code rate R less than channel capacity the codes have scalable error probability with an exponent E depending on C−R and scalable complexity with space complexity not more than a specific constant times n³, delay not more than a constant times log n, and time complexity rate not more than a constant, where the constants depend only on the power requirement and the channel specification (e.g., through the signal to noise ratio snr=P/σ²). [See, also “Structured Code System,” “Scaling of Error Probability,” and “Scaling of Code Complexity”]

Partitioned Superposition Code: A sparse superposition code in which the N columns of the matrix Xare partitioned into L sections of size B, with at most one column selected in each section. It is a structured code system, because a size (n′,L′) code is nested in a larger size (n,L) code by restricting to the use of the first L′<L sections and the first n′<n sections in both encoding and decoding. [See, also “Sparse Superposition Code” and “Structured Code System”]

Scaling of Code Complexity: For a specified sequence of code systems with specific hardware instantiation, the scaling of complexity is the expression of the size complexity, time complexity, and delay as typically non-decreasing functions of the code-length n. In formal computer science, feasibility is associated with there being a finite power of n that bounds these expressions of complexity. For communication industry purposes, implementation has more exacting requirements. The space complexity may coincide with expressions bounded b n² or n³ but certainly not n⁵ or greater. Refined space complexity expressions arise with specific codes in which there can be dependence on products of other measures of size (e.g., n, L and B for dictionary based codes invented herein). The time complexity rate needs to be constant to prevent computation from interfering with communication rate in the setting of a continuing sequence of received Y vectors. It is not known what are the best possible expressions for delay, it may be necessary to permit it to grow slowly with n, e.g., logarithmically, as is the case for the designs herein. [See, also “Code Complexity”]

Scaling of Error Probability: For a sequence of code systems of code-length n, an exponential error probability (also called exponentially small error probability or geometrically small error probability) is a probability of error not more than value of the form 10^(−nE), with a positive exponent E that conveys the rate of change of log probability with increasing code-length n. Probabilities bounded by values of the form n^(w)10^(−nE) are also said to be exponentially small, where w and E depend on characteristics of the code and the channel. When a sequence of code systems has error probability governed by an expression of the form 10^(−LE) indexed by a related parameter (such as the number of message sections L), that agrees with n to within a log factor, we still say that the error probability is exponentially small (or more specifically it is exponentially small in L). By Shannon theory, the exponent E can be positive only for code rates R<C.

Scaling of Section Mistake Rates: A code system with message sections is said to be mistake rate scalable if there is a decreasing sequence of target mistake rates approaching 0, said target mistake rate depending on s=log(B), such that the probability with which the mistake rate is greater than the target is exponentially small in L/s with exponent positive for any code rates R<C_(B) where C_(B) approaches the capacity C.

Section Error (also called a “section mistake”): When the message string is partitioned into sections (sub-blocks), a section error is the event that the corresponding portion of the decoded message is not equal to that portion of the message.

Section Error Rate: See, “Fraction of Section Mistakes.”

Section Mistake: See, “Section Error.”

Shannon Capacity: See, “Channel Capacity.”

Signal Power: A constraint on the transmission power of a communication system that translates to the average squared magnitude P of the sequence of n real or complex values to be sent.

Sparse Superposition Code: A code in which there is a design matrix for which codewords are linear combinations of not more than a specified number L of columns of X. The message is specified by the selection of columns (or equivalently by the selection of which coefficients of the linear combination are non-zero).

Structured Code System: A set of code systems, one for each block-length n, in which there is a nesting of ingredients of the encoder and decoder design of smaller block-lengths as specializations of the designs for larger block-lengths.

DESCRIPTION OF SPECIFIC ILLUSTRATIVE EMBODIMENTS OF THE INVENTION

FIG. 1 is a simplified schematic representation of a transmission system 100 having an encoder 120 constructed in accordance with the principles of the invention arranged to deliver data encoded as coefficients multiplied by values stored in a design matrix to a data channel 150 having a predetermined maximum transmission capacity, the encoded data being delivered to a decoder 160 that contains a local copy of the design matrix 165 and a coefficient extraction processor 170.

Encoder 120 contains in this specific illustrative embodiment of the invention a logic unit 125 that receives message bits u=(u₁, u₂, . . . u_(K)) and maps these into a sequence of flags (one for each column of the dictionary), wherein a flag=1 specifies that column is to be included in the linear combination (with non-zero coefficient) and flag=0 specifies that column is excluded (i.e., is assigned a zero coefficient value).

In the specific embodiment of partitioned superposition codes, in each section of the partition there is only one out of B column selected to have flag=1. In this embodiment, the action of logic unit 125 is to parse the message bit stream into segments each having log₂(B) bits, said bit stream segments specifying (selecting, flagging) the memory address of the one chosen column in the corresponding section of a design matrix 130, said selected column to be included in the superposition with a non-zero coefficient value, whereas all non-selected columns are not included in the superposition (i.e., have a zero coefficient value and a flag value set to 0).

It is a highly advantageous aspect of the present invention that those who would design a communications system in accordance with the principles of the present invention are able to use any size of design matrix. More specifically, as the size of the design matrix is increased, the size of the input stream K=L log(B) and the codelength n are increased (wherein the ratio is held fixed at code rate R=K/n), said increase in K and n resulting in an exponentially decreasing probability of decoding error, as will be described in detail in a later section of this disclosure. Such adaptability of the present communications system enables the system to be tailored to the quality of the receiving device or to the error requirements of the particular application.

Design matrix 130 is in the form of a memory that contains random values X (not shown). In this embodiment of the invention, a succession of flags of columns for linear combination, as noted above, are received by the design matrix from logic unit 125, wherein columns with flag=1 are included in the linear combination (with non-zero coefficient value) and columns with flag=0 are excluded from the linear combination (zero coefficient value). The information that is desired to be transmitted, i.e., the message, is thereby contained in the choice of the subset included with non-zero coefficients. Each coefficient is combined with a respectively associated one of the columns of X contained in design matrix 130, and said columns are superimposed thus forming a code word Xβ=β₁X₁+β₂X₂+ . . . β_(N)X_(N). Thus, the data that is delivered to data channel 150 comprises a sequence of linear combinations of the selected columns of the design matrix X

Data channel 150 is any channel that takes real or complex inputs and produces real or complex received values, subjected to noise, said data channel encompassing several parts of a standard communication system (e.g., the modulator, transmitter, transmission channel, receiver, filter, demodulator), said channel having a Shannon capacity, as herein above described. Moreover, it is an additive noise channel in which the noise may be characterized as having a Gaussian distribution. Thus, a code word Xβ is issued by encoder 120, propagated along noisy data channel 150, and the resulting vector Y is received at decoder 160, where Y is the sum of the code word plus the noise, i.e., Y=Xβ+ξ, where ξ is the noise vector.

Decoder 160, in this simplistic specific illustrative embodiment of the invention, receives Y and subjects it to a decoding process directed toward the identification and extraction of the coefficients β_(f). More specifically, a coefficient extraction processor 170, which ideally would approximate the functionality of a theoretically optimal least squares decoder processor, would achieve the stochastically smallest distribution of mistakes. A comparison of the present practical decoder to the theoretically optimum least squares decoder of the sparse superposition codes is set forth in a subsequent section of this disclosure.

In addition to the foregoing, decoder 160 is, in an advantageous embodiment of the invention, provided with an update function generator 175 that is useful to determine an update function g_(L)(x) that identifies the likely performance of iterative decoding steps. In accordance with this aspect of the invention, the update function g_(L)(x) is responsive to signal power allocation. In a further embodiment of the invention, update function g_(L)(x) is responsive to the size B and the number L of columns of the design matrix. In a still further embodiment, the update function is responsive to a rate characteristic R of data transmission and to a signal-to-noise ratio (“snr”) characteristic of the data transmission. Specific characteristics of update function g_(L)(x) are described in greater detail below in relation to FIGS. 4-6.

FIG. 2 is a simplified schematic representation of a transmission system 200 constructed in accordance with the principles of the invention. Elements of structure or methodology that have previously been discussed are similarly designated. Transmission system 200 is provided with encoder 120 arranged to deliver data encoded as coefficients multiplied by values stored in a design matrix to a data channel 150 having a predetermined maximum transmission capacity, as described above.

The encoded data is delivered to a decoder 220 that contains a local copy of a design matrix 165 and a control processor 240 that functions in accordance with the steps set forth in function block 250, denominated “adaptive successive decoding.” Local design matrix 165 is substantially identical to encoder design matrix 130. As shown in this figure, adaptive successive decoding at function block 250 includes the steps of:

-   -   Computing inner products of Y with respectively associated         columns of X from local design matrix 230;     -   Identifying ones of the inner products that exceed a         predetermined threshold value;     -   Forming an initial fit;     -   Computing iteratively inner products of residuals Y−fit_(k−1)         with each remaining column of X;     -   Identifying the columns for which the inner products exceed a         predetermined threshold value and adding them to the fit; and     -   Stopping the process at a step where k is a specific multiple of         log B, or when no inner products exceed the threshold.

FIG. 3 is a simplified schematic representation of a transmission system 300 constructed in accordance with the principles of the invention. Elements of structure that have previously been discussed are similarly designated. As previously noted, an encoder 120 delivers data encoded in the form of a string of flags specifying a selection of a set of non-zero coefficients, as hereinabove described, which linearly combine columns of X stored in a design matrix 125, to data channel 150 that, as noted, has a predetermined maximum capacity. The encoded data is delivered to a decoder system 320 that contains a plurality of local copies of a design matrix, each associated with a sequential decoding processor, the combination of local design matrix and processor being designated 330-1, 330-2, to 330-k. Decoder system 320 is constructed in accordance with the principles of the invention to achieve continuous data decoding.

In operation, a vector Y corresponding to a received signal is comprised of an original codeword Xβ plus a noise vector ξ. Noise vector ξ derives from data channel 150, and therefore Y=Xβ+ξ. Noise vector ξ may have a Gaussian distribution. Vector Y is delivered to the processor in 350-1 where its inner product is computed with each column of X in associated local design matrix 330-1. As set forth in the steps in function block 350-1, each inner product then is compared to a predetermined threshold value, and if it exceeds the threshold, it is flagged and the associated column are superimposed in a fit. The fit is then subtracted from Y, leaving a residual that is delivered to processor 330-2.

Upon delivery of the residual to processor 330-2, a process step 360 causes a new value of Yto be delivered from data channel 150 to processor 330-1. Thus, the processors are continuously active computing residuals.

Processor 330-2 computes the inner product of the residual it received from processor 330-1, in accordance with the process of function block 350-2. This is effected by computing the inner product of the residual with not already flagged column of Xin the local design matrix associated with processor 330-2. Each of these inner products then is compared to a predetermined threshold value, and if it exceeds the threshold, each such associated column is flagged and added to the fit. The fit is then subtracted from the residual, leaving a further residual that is delivered to the processor 330-3 (not shown). The process terminates when no columns of X have inner product with the residual exceeding the threshold value, or when processing has been completed by processor 330-k. At which point the sequence of addresses of the columns flagged as having inner product above threshold provide the decoded message.

1. Introduction

For the additive white Gaussian noise channel with average codeword power constraint, sparse superposition codes are developed, in which the encoding and decoding are computationally feasible, and the communication is reliable. The codewords are linear combinations of subsets of vectors from a given dictionary, with the possible messages indexed by the choice of subset. An adaptive successive decoder is developed, with which communication is demonstrated to be reliable with error probability exponentially small for all rates below the Shannon capacity.

The additive white Gaussian noise channel is basic to Shannon theory and underlies practical communication models. Sparse superposition codes for this channel are introduced and analyzed and fast encoders and decoders are here invented for which error probability is demonstrated to be exponentially small for any rate below the capacity. The strategy and its analysis merges modern perspectives on statistical regression, model selection and information theory.

The development here provides the first demonstration of practical encoders and decoders, indexed by the size n of the code, with which the communication is reliable for any rate below capacity, with error probability demonstrated to be exponentially small in n and the computational resources required, specified by the number of memory positions and the number of simple processors, is demonstrated to be a low order power of n, and the processor computation time is demonstrated to be a constant per received symbol.

Performance-scalability is used to refer succinctly to the indicated manner in which the error probability, the code rate, and the computational complexity together scale with the size n of the code.

Such a favorable scalability property is essential to know for a code system, such that if it is performing at given code size how the performance will thereafter scale (for instance in improved error probability for a given fraction of capacity if code size is doubled) to suitably take advantage of increased computational capability (increased computer memory and processors) sufficient to accommodate the increased code size.

A summary of this work appeared in (Barron and Joseph, ‘Toward fast reliable communication at rates near capacity with Gaussian noise’, IEEE Intern. Symp. Inform. Theory, June 2010), after the date of first discloser, and thereafter an extensive manuscript has been made publicly available (Barron and Joseph, ‘Sparse Superposition Codes: Fast and Reliable at Rates Approaching Capacity with Gaussian Noise’, www.stat.yale.edu/˜arb4 publications), upon which the present patent manuscript is based. Companion work by the inventors (Barron and Joseph, ‘Least squares superposition coding of moderate dictionary size, reliable at rates up to channel capacity’ IEEE Intern. Syrup. Inform. Theory, June 2010) has theory for the optimal least squares decoder again with exponentially small error probability for any rate below capacity, though that companion work, like all previous theoretical capacity-achieving schemes is lacking in practical decodability. In both treatments (that given here for practical decoding and that given for impractical optimal least squares decoding) the exponent of the error is shown to depend on the difference between the capacity and the rate. Here for the practical decoder the size of the smallest gap from capacity is quantified in terms of design parameters of the code thereby allowing demonstration of rate approaching capacity as one adjusts these design parameters.

In the familiar communication set-up, an encoder is to map input bit strings u=(u₁, u₂, . . . , u_(K)) of length K into codewords which are length n strings of real numbers c₁, c₂, . . . , c_(n) of norm expressed via the power (1/n)Σ_(i=1) ^(n)c_(i) ². The average of the power across the 2^(K) codewords is to be not more than P. The channel adds independent N(0, σ²) noise to the codeword yielding a received length n string Y. A decoder is to map it into an estimate û desired to be a correct decoding of u. Block error is the event û≠u. When the input string is partitioned into sections, the section error rate is the fraction of sections not correctly decoded. The reliability requirement is that, with sufficiently large n, the section error rate is small with high probability or, more stringently, the block error probability is small, averaged over input strings a as well as the distribution of Y. The communication rate R=K/n is the ratio of the number of message bits to the number of uses of the channel required to communicate them.

The supremum of reliable rates of communication is the channel capacity given by

=(½)log₂(1+P/σ²), by traditional Shannon information theory. For practical coding the challenge is to achieve arbitrary rates below the capacity, while guaranteeing reliable decoding in manageable computation time.

In a communication system operating at rate R, the input bit strings arise from input sequences u₁, u₂, . . . cut into successive K bit strings, each of which is encoded and sent, leading to a succession of received length n strings Y. The reliability aim that the block error probability be exponentially small is such that errors are unlikely over long time spans. The computational aim is that coding and decoding computations proceed on the fly, rapidly, with the decoder having not too many pipelined computational units, so that there is only moderate delay in the system.

The development here is specific to the discrete-time channel for which Y_(i)=c_(i)+ε_(i) for i=1, 2, . . . , n with real-valued inputs and outputs and with independent Gaussian noise. Standard communication models, even in continuous-time, have been reduced to this discrete-time white Gaussian noise setting, or to parallel uses of such, when there is a frequency band constraint for signal modulation and when there is a specified spectrum of noise over that frequency band. Solution to the coding problem, when married to appropriate modulation schemes, is regarded as relevant to myriad settings involving transmission over wires or cables for internet, television, or telephone communications or in wireless radio, TV, phone, satellite or other space communications.

Previous standard approaches, as discussed in Formey and Ungerboeck (IEEE Trans. Inform. Theory 1998), entail a decomposition into separate problems of modulation, of shaping of a multivariate signal constellation, and of coding. For coding purposes, the continuous-time modulation and demodulation may be regarded as given so that the channel reduces to the indicated discrete-time model. In the approach developed in the invention here-in the shaping of the signal is built directly into the code design and not handled separately.

As shall be reviewed in the section on past work below, there are practical schemes of specific size with empirically good performance.

However, all past works concerning sequences of practical schemes, with rates set arbitrarily below capacity, lack proof that the error probability will exponentially small in the size n of the code, wherein the exponent of the error probability will depend on the difference between the capacity and the rate. With the decoder invented herein, it amenable to the desired analysis, providing the first theory establishing that a practical scheme is reliable at rates approaching capacity for the Gaussian channel.

1.1 Sparse Superposition Codes:

The framework for superposition codes is the formation of specific forms of linear combinations of a given list of vectors. This list (or book) of vectors is denoted X₁, X₂, . . . , X_(N). Each vector has n real-valued (or complex-valued) coordinates, for which the codeword vectors take the form of superpositions

β1 X ₁+β₂ X ₂+β_(N) X _(N).

The vectors X_(j) provide the terms or components of the codewords with coefficients β_(j). By design, each entry of these vectors X_(j) is independent standard normal. The choice of codeword is conveyed through the coefficients, with sum of squares chosen to match the power requirement P. The received vector is in accordance with the statistical linear model

Y=Xβ+ε,

where X is the matrix whose columns are the vectors X₁, X₂, . . . , X_(N) and ε is the noise vector with distribution N(0,σ²I). In some channel models it can be convenient to allow codeword vectors and received vectors to have complex-valued entries, though as there is no substantive difference in the analysis for that case, the focus in the description unfolding here is on the real-valued case. The book X is called the design matrix consisting of p=N variables, each with n observations, and this list of variables is also called the dictionary of candidate terms.

For general subset superposition coding the message bit string is arranged to be conveyed by mapping it into a choice of a subset of terms, called sent, with L coefficients non-zero, with specified positive values. Denote B=N/L to be the ratio of dictionary size to the number of terms sent. When B is large, it is a sparse superposition code. In this case the number of terms sent is a small fraction L/N of the dictionary size.

In subset coding, it is known in advance to the encoder and decoder what will be the coefficient magnitude √{square root over (P_(j))} if a term is sent. Thus β_(j) ²=P_(j)1_(jsent) is equal to P_(j) if the term is sent and equal to 0 otherwise. In the simplest case, the values of the non-zero coefficients are the same, with P_(j)=P/L.

Optionally, the non-zero coefficient values may be +1 or −1 times specified magnitudes, in which case the superposition code is said to be signed. Then the message is conveyed by the sequence of signs as well as the choice of subset.

For subset coding, in general, the set of permitted coefficient vectors β is not an algebraic field, that is, it is not closed under linear operations. For instance, summing two coefficient vectors with distinct sets of L non-zero entries does not yield another such coefficient vector. Hence the linear statistical model here does not correspond to a linear code in the sense of traditional algebraic coding.

In this document particular focus is given to a specialization which the inventors herein call a partitioned superposition code. Here the book X is split into L sections of size B with one term selected from each, yielding L terms in each codeword. Likewise, the coefficient vector β is split into sections, with one coordinate non-zero in each section to indicate the selected term. Partitioning simplifies the organization of the encoder and the decoder.

Moreover, partitioning allows for either constant or variable power allocation, with P_(j) equal to values P_((l)) for j in section l, where Σ_(l) ^(L)P/(l)=P. This respects the requirement that Σ_(jsent)P_(j)=P, no matter which term is selected from each section. Set weights π_(j)=P_(j)/P. For any set of terms, its size induced by the weights is defined as the stun of the π_(j) for j in that set. Two particular cases investigated include the case of constant power allocation and the case that the power is proportional to

for sections l=1, 2, . . . , L. These variable power allocations are used in getting the rate up to capacity.

Most convenient with partitioned codes is the case that the section size B is a power of two. Then an input bit string a of length K=L log₂ B splits into L substrings of size log₂ B and the encoder becomes trivial. Each substring of u gives the index (or memory address) of the term to be sent from the corresponding section.

As said, the rate of the code is R=K/n input bits per channel uses, with arbitrary rate R less than

. For the partitioned superposition code this rate is

$R = {\frac{L\; \log \; B}{n}.}$

For specified L, B and R, the codelength n is (L/R)log B. This the length n and the subset size L agree to within a log factor.

Control of the dictionary size is critical to computationally advantageous coding and decoding. Possible dictionary sizes are between the extremes K and 2^(K) dictated by the number and size of the sections, where K is the number of input bits. In one extreme, with 1 section of size B=2^(K), the design X is the whole codebook with its columns as the codewords, but the exponential size makes its direct use impractical. At the other extreme there would be L=K sections, each with two candidate terms in subset coding or two signs of a single term in sign coding with B=1; in which case X is the generator matrix of a linear code.

Between these extremes, computationally feasible, reliable, high-rate codes are constructed with codewords corresponding to linear combinations of subsets of terms in moderate size dictionaries, with fast decoding algorithms. In particular, for the decoder developed here, at a specific sequence of rates approaching capacity, the error probability is shown to be exponentially small in L/(log B)^(3/2).

For high rate, near capacity, the analysis herein requires B to be large compared to (1+snr)² and for high reliability it also requires L to be large compared to (1+snr)², where snr=P/σ² is the signal to noise ratio.

Entries of X are drawn independently from a normal distribution with mean zero and a variance 1 so that the codewords Xβ have a Gaussian shape to their distribution and so that the codewords have average power near P. Other distributions for the entries of X may be considered, such as independent equiprobable ±1, with a near Gaussian shape for the codeword distribution obtained by the convolutions associated with sums of terms in subsets of size L.

There is some freedom in the choice of scale of the coefficients. Here the coordinates of the X_(j) are arranged to have variance 1 and the coefficients of β are set to have sura of squares equal to P. Alternatively, one may simplify the coefficient representation by arranging the coordinates of X_(j) to be normal with variance P_(j) and setting the non-zero coefficients of β to have magnitude 1. Whichever of these scales is convenient to the argument at hand is permitted.

1.2 Summary of Findings:

A fast sparse superposition decoder is herein described and its properties analyzed. The inventor herein call it adaptive successive decoding.

For computation, it is shown that with a total number of simple parallel processors (multiplier-accumulators) of order n B, and total memory work space of size n² B, it runs in a constant time per received symbol of the string Y.

For the communication rate, there are two cases. First, when the power of the terms sent are the same at P L in each section, the decoder is shown to reliably achieves rates up to a rate R₀=(½)P/(P+σ²) which is less than capacity. It is close to the capacity when the signal-to-noise ratio is low. It is a deficiency of constant power allocation with the scheme here that its rate will be substantially less than the capacity if the signal-to-noise is not low.

To bring the rate higher, up to capacity, a variable power allocation is used with power P_((l)) proportional to

, for sections l from 1 to L, with improvements from a slight modification of this power allocation for l/L near 1.

To summarize what is achieved concerning the rate, for each B≧2, there is a positive communication rate

_(B) that the decoder herein achieves with large L. This

_(B) depends on the section size B as well as the signal to noise ratio snr=P/σ². It approaches the Capacity

=(½)log(1+snr) as B increases, albeit slowly. The relative drop from capacity

${\Delta_{B} = \frac{C - C_{B}}{C}},$

is accurately bounded, except for extremes of small and large snr, by an expression near

$\frac{\left( {1.5 + {1/v}} \right)\log \; \log \; B}{\log \; B},$

where

=snr/(1+snr), with other bounds given to encompass accurately also the small and large snr cases.

Concerning reliability, a positive error exponent function ε(

_(B)−R) is provided for R<

_(B). It is of the order (

_(B)−R)²√{square root over (log B)} for rates R near

_(B). The sparse superposition code reliably makes not more than a small fraction of section mistakes. Combined with an outer Reed-Solomon code to correct that small fraction of section mistakes the result is a code with block error probability bounded by an expression exponentially small in Lε(

C_(B)−R), which is exponentially small in nε(

_(B)−R)/log B. For a range of rates R not far from

_(B), the error exponent is shown to be within a √{square root over (log B)} factor of the optimum reliability exponent.

1.3 Decoding Sparse Superposition Codes:

Optimal decoding for minimal average probability of error consists of finding the codeword Xβ with coefficient vector β of the assumed form that maximizes the posterior probability, conditioning on X and Y. This coincides, in the case of equal prior probabilities, with the maximum likelihood rule of seeking such a codeword to minimize the smn of squared errors in fit to Y. This is a least squares regression problem min_(β)∥Y−X≈∥², with codeword constraints on the coefficient vector. There is the concern that exact least squares decoding is computationally impractical. Performance bounds for the optimal decoder are developed in the previously cited companion work achieving rates up to capacity in the constant power allocation case. Instead, here a practical decoder is developed for which desired properties of reliability and rate approaching capacity are established in the variable power allocation case.

The basic step of the decoder is to compute for a given vector, initially the received string Y, its inner product with each of the terms in the dictionary, as test statistics, and see which of these inner products are above a threshold. Such a set of inner products for a step of the decoder is performed in parallel by a computational unit, e.g. a signal-processing chip with N=LB parallel accumulators, each of which has pipelined computation, so that the inner product is updated as the elements of the string arrive.

In this basic step, the terms that it decodes are among those for which the test statistic is above threshold. The step either selects all the terms with inner product above threshold, or a portion of these with specified total weight. Having inner product X_(j) ^(T)Y above a threshold T=∥Y∥_(τ) corresponds to having normalized inner product X_(j) ^(T)Y/∥Y∥ above a threshold τ set to be of the form

τ=√{square root over (2 log B)}+a,

where the logarithm is taken using base e. This threshold may also be expressed as √{square root over (2 log B)}(1+δ_(a)) with δ_(a)=a/√{square root over (2 log B)}. The a is a positive value, free to be specified, that impacts the behavior of the algorithm by controlling the fraction of terms above threshold each step. An ideal value of a is moderately small, corresponding to δ_(a) near 0.75(log log B)/log B, plus log(1+snr)/log B when snr is not small. Having 2δ_(a) near 1.5 log log B/log B plus 4

/log B constitutes a large part of the above mentioned rate drop Δ_(B).

Having the threshold larger than √{square root over (2 log B)} implies that the fraction of incorrect terms above threshold is negligible. Yet it also means that only a moderate fraction of correct terms are found to be above threshold each step.

A fit is formed at the end of each step by adding the terms that were selected. Additional steps are used to bring the total fraction decoded up near 1.

Each subsequent step of the decoder computes updated test statistics, taking inner products of the remaining terms with a vector determined using Y and the previous fit, and sees which are above threshold. For fastest operation these updates are performed on additional computational units so as to allow pipelined decoding of a succession of received strings. The test statistic can be the inner product of the terms X_(j) with the vector of residuals equal to the difference of Y and the previous fit. As will be explained, a variant of this statistic is developed and found to be somewhat simpler to analyze.

A key feature is that the decoding algorithm does not pre-specify which sections of terms will be decoded on any one step. Rather it adapts the choice in accordance with which sections have a term with an inner product observed to be above threshold. Thus this class of procedures is called adaptive successive decoding.

Concerning the advantages of variable power in the partitioned code case, which allows the scheme herein to achieve rates near capacity, the idea is that the power allocations proportional to

give some favoring to the decoding of the higher power sections among those that remain each step. This produces more statistical power for the test initially as well as retaining enough discrimination power for subsequent steps.

Such power allocation also would arise if one were attempting to successively decode one section at a time, with the signal contributions of as yet un-decoded sections treated as noise, in a way that splits the rate

into L pieces each of size

/L; however, such pre-specification of one section to decode each step would require the section sizes to be exponentially large to achieve desired reliability. In contrast, in the adaptive scheme herein, many of the sections are considered each step. The power allocations do not change too much across many nearby sections, so that a sufficient distribution of decodings can occur each step.

For rate near capacity, it helpful to use a modified power allocation, with power proportional to

${\max \left\{ {^{{- 2}C\frac{ - 1}{L}},u_{cut}} \right\}},$

where u_(cut)=

(1+δ_(c)) with a small non-negative value of δ_(c). Thus u_(cut) can be slightly larger than

. This modification performs a slight leveling of the power allocation for l/L near 1. It helps ensure that, even in the end game, there will be sections for which the true terms are expected to have inner product above threshold.

Analysis of empirical bounds on the proportions of correct detections involves events shown to be nearly independent across the L sections. The probability with which such proportions differ much from what is expected is exponentially small in the number of sections L. In the case of variable power allocation the inductive determination of distributional properties herein is seen to requires that one work with weighted proportions of events, which are sums across the terms of indicators of events multiplied by the weights provided by π_(j)=P_(j)/P. With bounded ratio of maximum to minimum power across the sections, such weighted proportions agree with un-weighted proportions to within constant factors. Moreover, for indicators of independent events, weighted proportions have similar exponential tail bounds, except that in the exponent, in place of L there is L_(π)=1/max_(j)π_(j), which is approximately a constant multiple of L for the designs investigated here.

1.4 An Update Function:

A key ingredient of this work is the determination of a function g_(L):[0, 1]→[0, 1], called the update function, which depends on the design parameters (the power allocation and the parameters L, B and R) as well as the snr. This function g_(L)(x) determines the likely performance of successive steps of the algorithm. Also, for a variant of the residual-based test statistics, it is used to set weights of combination that determine the best updates of test statistics.

Let {circumflex over (q)}_(k) ^(tot) denote the weighted proportion correctly decoded after k steps. A sequence of deterministic values q_(1,k) is exhibited such that {circumflex over (q)}_(k) ^(tot) is likely to exceed q_(1,k) each step. The q_(1,k) is near the value g_(L)(q_(1,k−1)) given by the update function, provided the false alarm rate is maintained small. Indeed, an adjusted value q_(1,k) ^(adj) is arranged to be not much less than g_(L)(q_(1,k−1) ^(adj)) where the ‘adj’ in the superscript denotes an adjustment to q_(1,k) to account for false alarms.

Determination of whether a particular choice of design parameters provides a total fraction of correct detections approaching 1 reduces to verification that this function g_(L)(x) remains strictly above the line y=x for some interval of the form [0,x*] with x* near 1. The successive values of thea g_(L)(x_(k))−x_(k) at x_(k)=q_(1,k−1) ^(adj) control the error exponents as well as the size of the improvement in the detection rate and the number of steps of the algorithm. The final weighted fraction of failed detections is controlled by 1−g_(L)(x*).

The role of g_(L) is shown in FIG. 4. This figure provides a plot of the function g_(L)(x) in a specific case. The dots indicate the sequence q_(1,k) ^(adj) for 16 steps. Here B=2¹⁶, snr=7, R=0.74 and L taken to be equal to B. The height reached by the g_(L)(x) curve at the final step corresponds to a weighted correct detection rate target of 0.993, un-weighted 0.986, for a failed detection rate target of 0.014. The accumulated false alarm rate bound is 0.008. The probability of mistake rates larger than these targets is bounded by 4.8×10⁻⁴.

Provision of g_(L)(x) and the computation of its iterates provides a computational devise by which a proposed scheme is checked for its capabilities.

An equally important use of g_(L)(x) is analytical analysis of the extent of positivity of the gap g_(L)(x)−x depending on the design parameters. For any power allocation there will be a largest rate R at which the gap remains positive over most of the interval [0, 1] for sufficient size L and B. Power allocations with P_((l)) proportional to

, or slight modifications thereof, are shown to be the form required for the gap g_(L)(x)−x to have such positivity for rates R near

.

Analytical examination of the update function shows for large L how the choice of the rate R controls the size of the shortfall 1−x* as well as the minimum size of the gap g_(L)(x)−x for 0≦x≦x*, as functions of B and snr. Thereby bounds are obtained on the mistake rate, the error exponent, and the maximal rate for which the method produces high reliability.

To summarize, with the adaptive successive decoder and suitable power allocation, for rates approaching capacity, the update function stays sufficiently above x over most of [0, 1] and, consequently, the decoder has a high chance of not more than a small fraction of section mistakes.

1.5 Accounting of Section Mistakes:

Ideally, the decoder selects one term from each section, producing an output which is the index of the selected term. It is not in error when the term selected matches the one sent.

In a section a mistake occurs from an incorrect term above threshold (a false alarm) or from failure of the correct term to provide a statistic value above threshold after a suitable number of steps (a failed detection). Let {circumflex over (δ)}_(mis) refer to the failed detection rate plus the false alarm rate, that is, the sum of the fraction of section with failed detections and the fraction of sections with false alarms. This sum from the two sources of mistake is at least the fraction of section mistakes, recognizing that both types can occur. The technique here controls this {circumflex over (δ)}_(mis) by providing a small bound δ_(mis) that holds with high probability.

A section mistake is counted as an error if it arises from a single incorrectly selected term. It is an erasure if no term is selected or more than one term is selected. The distinction is that a section error is a mistake you don't know you made and a section erasure is one you known you made. Let {circumflex over (δ)}_(error) be the fraction of section errors and {circumflex over (δ)}_(erase) be the fraction of section erasures. In each section one sees that the associated indicators of events satisfy the property that 1_(erase)+2 1_(error) is not more than 1_(failed detection)+1_(false alarm). This is because an error event requires both a failed detection and a false alarm. Accordingly 2{circumflex over (δ)}_(error)+{circumflex over (δ)}_(erase) is not more than {circumflex over (δ)}_(mis), the failed detection rate plus the false alarm rate.

1.6 An Outer Code:

An issue with this superposition scheme is that candidate subsets of terms sent could differ from each other in only a few sections. When that is so, the subsets could be difficult to distinguish, so that it would be natural to expect a few section mistakes.

An approach is discussed which completes the task of identifying the terms by arranging sufficient distance between the subsets, using composition with an outer Reed-Solomon (RS) code of rate near one. The alphabet of the Reed-Solomon code is chosen to be a set of size B, a power of 2. Indeed in this invention it is arranged the RS symbols correspond to the indices of the selected terms in each section. Details are given in a later section. Suppose the likely event {circumflex over (δ)}_(mis)<δ_(mis) holds from the output of the inner superposition code. Then the outer Reed-Solomon corrects the small fraction of remaining mistakes so that the composite decoder ends up not only with small section mistake rate but also with small block error probability. If R_(outer)=1−δ is the communication rate of an RS code, with 0<δ<1, then the section errors and erasures can be corrected, provided δ_(mis)≦δ.

Furthermore, if R_(inner) is the rate associated with the inner (superposition) code, then the total rate after correcting for the remaining mistakes is given by R_(total)=R_(inner)R_(outer), using δ=δ_(mis). Moreover, if Δ_(inner) is the relative rate drop from capacity of the inner code, then the relative rate drop of the composite code Δ_(total) is not more than δ_(mis)+Δ_(inner).

The end result, using the theory developed herein for the distribution of the fraction of mistakes of the superposition code, is that for suitable rate up to a value near capacity the block error probability is exponentially small.

One may regard the composite code as a superposition code in which the subsets are forced to maintain at least a certain minimal separation, so that decoding to within a certain distance from the true subset implies exact decoding.

Performance of the sparse superposition code is measured by the three fundamentals of computation, rate, and reliability.

1.7 Computational Resource of Hardware Implementation:

The main computation required of each step of the decoder is the computation of the inner products of the residual vectors with each column of the dictionary. Or one has computation of related statistics which require the same order of resource. For simplicity in this subsection the case is described in which one works with the residuals and accepts each term above threshold. The inner products requires order nLB multiply-and-adds each step, yielding a total computation of order nLBm for m steps. The ideal number of steps m according to the bounds obtained herein is not more than 2+snr log B.

When there is a stream of strings Y arriving in succession at the decoder, it is natural to organize the computations in a parallel and pipelined fashion as follows. One allocates m signal processing chips, each configured nearly identically, to do the inner products. One such chip does the inner products with Y, a second chip does the inner products with the residuals from the preceding received string, and so on, up to chip m which is working on the final decoding step from the string received several steps before.

Each signal processing chip has in parallel a number of simple processors, each consisting of a multiplier and an accumulator, one for each stored column of the dictionary under consideration, with capability to provide pipelined accumulation of the required sum of products. This permits the collection of inner products to be computed online as the coordinates of the vectors are received. After an initial delay of m received strings, all m chips are working simultaneously.

Moreover, for each chip there is a collection of simple comparators, which compare the computed inner products to the threshold and store, for each column, a flag of whether it is to be part of the update. Sums of the associated columns are computed in updating the residuals (or related vectors) for the next step. The entries of that simple computation (sums of up to L values) are to be provided for the next chip before processing the entries of the next received vector. If need be, to keep the runtime at a constant per received symbol, one arranges 2m chips, alternating between inner product chips and subset sum chips, each working simultaneously, but on strings received up to 2m steps before. The runtime per received entry in the string Y is controlled by the time required to load such an entry (or counterpart residuals on the additional chips) at each processor on the chip and perform in parallel the multiplication by the associated dictionary entries with result accumulated for the formation of the inner products.

The terminology signal processing chip refers to computational units that run in parallel to perform the indicated tasks. Whether one or more of these computational units fit on the same physical computer chip depends on the size of the code dictionary and the current scale of circuit integration technology, which is an implementation matter not a concern at the present level of decoder description.

If each of the signal processing chips keeps a local copy of the dictionary X, alleviating the challenge of numerous simultaneous memory calls, the total computational space (memory positions) involved in the decoder is nLBm, along with space for LBm multiplier-accumulators, to achieve constant order computation time per received symbol. Naturally, there is the alternative of increased computation time with less space; indeed, decoding by serial computation would have runtime of order nLBm.

Substituting L=nR/log B and m of order log B the computational resource expression nLBm simplifies. One sees that the total computational resource required (either space or time) is of order n²B for this sparse superposition decoder. More precisely, to include the effect of the snr on the computational resource, using the number of steps m which arise in upcoming bounds, which is within 2 of snr log B, and using R upper bounded by capacity

, the computational resource of nLBm memory positions is bounded by

snr n² B, and a number LBm of multiplier-adders bounded by

snr n B.

In concert with the action of this decoder, the additional computational resource of a Reed-Solomon decoder acts on the indices of which term is flagged from each section to provides correction of the few mistakes. The address of which term is flagged in a section provides the corresponding symbol for the RS decoder, with the understanding that if a section has no term flagged or more than one term flagged it is treated as an erasure. For this section, as the literature on RS code computation is plentiful, it is simply note that the computation resource required is also bounded as a low order polynomial in the size of the code.

1.8 Achieved Rate:

This subsection discusses the nature of the rates achieved with adaptive successive decoding. The invention herein achieves not only fixed rates R<

but also rates R up to

_(B), for which the gap from capacity is of the order near 1/log B.

Two approaches are provided for evaluation of how high a rate R is achieved. For any L, B, snr, any specified error probability and any small specified fraction of mistakes of the inner code, numerical computation of the progression of g_(L)(x) permits a numerical evaluation of the largest R for which g_(L)(x) remains above x sufficiently to achieve the specified objectives.

The second approach is to provide simplified bounds to prove analytically that the achieved rate is close to capacity, and exhibit the nature of the closeness to capacity as function of snr and B. This is captured by the rate envelope C_(B) and bounds on its relative rate drop Δ_(B). Here contributions to Δ_(B) are summarized, in a way that provides a partial blueprint to later developments. Fuller explanation of the origins of these contributions arise in these developments in later sections.

The update function g_(L)(x) is near a function g(x), with difference bounded by a multiple of 1/L. Properties of this function are used to produce a rate expression that ensures that g(x) remains above x, enabling the successive decoding to progress reliability. In the full rate expressions developed later, there are quantities η, h, and ρ that determine error exponents multiplied by L. So for large enough L, these exponents can be taken to be arbitrarily small. Setting those quantities to the values for which these exponents would become 0, and ignoring terms that are small in 1/L, provides simplification giving rise to what the inventors call the rate envelope, denoted C_(B).

With this rate envelope, for R<C_(B), these tools enable us to relate the exponent of reliability of the code to a positive function of C_(B)−R times L, even for L finite.

There are two parts to the relative rate drop bound Δ_(B), which are written as Δ_(shape) plus Δ_(alarm), with details on these in later sections. Here these contributions are summarized to express the form of the bound on Δ_(B).

The second part denoted Δ_(alarm) is determined by optimizing a combination of rate drop contributions from 2δ_(a), plus a term snr/(m−1) involving the number of steps m, plus terms involving the accumulated false alarm rate. Using the natural logarithm, this Δ_(alarm) is optimized at m equal to an integer part of 2+snr log B and an accumulated baseline false alarm rate of 1/[(3

+½) log B]. At this optimized m and optimized false alarm rate, the value of the threshold parameter δ_(a) is

$\delta_{a} = \frac{\log \left\lbrack {m\; {{snr}\left( {3 + {{1/2}C}} \right)}{\sqrt{\log \; B}/\sqrt{4\pi}}} \right\rbrack}{2\; \log \; B}$ and $\Delta_{alarm} = {{2\delta_{a}} + {\frac{2}{\log \; B}.}}$

At the optimized m, the δ_(a) is an increasing function of snr, with value approaching 0.25 log [(log B)/π]/log B for small snr and value near

$\frac{{{.75}\mspace{14mu} \log \; \log \; B} + {2C} - {0.25\mspace{14mu} {\log \left( {4{\pi/9}} \right)}}}{\log \; B}$

for moderately large snr. The constant subtracted in the numerator 0.25 log(4π/9) is about 0.08. With δ_(a) thus set, it determines the value of the threshold τ=√{square root over (2 log B)}(1+δ_(a)).

To obtain a small 2δ_(a), and hence small Δ_(alarm), this bound requires log B large compared to 4

, which implies that the section size B is large compared to (1+snr².

The Δ_(shape) depends on the choice of the variable power allocation rule, via the function g and its shape. For a specified power allocation, it is determined by a minimal inner code rate drop contribution at which the function has a non-negative gap g(x)−x on [0, x*], plus the contribution to the outer code rate drop associated with the weighted proportion not detected δ*=1−g(x*). For determination of Δ_(shape), three cases for power allocation are examined, then, for each snr, pick the one with the best such tradeoff, which includes determination of the best x*. The result of this examination is a Δ_(shape) which is a decreasing function of snr.

The first case has no leveling (δ_(c)=0). In this case the function g(x)−x is decreasing for suitable rates. Using an optimized x* it provides a candidate Δ_(shape) equal to 1/τ² plus

/(τ

), where

is an explicitly given expression with value near

for large

. If snr is not large, this case does not accomplish the aims because the term involving 1/τ, near 1/√{square root over (2 log B)}, is not nearly as small as will be demonstrated in the leveling case. Yet with snr such that

is large compared to τ, this Δ_(shape) is acceptable, providing a contribution to the rate drop near the value 1/(2 log B). Then the total rate drop is primarily determined by Δ_(alarm), yielding, for large snr, that Δ_(B) is near

$\frac{{1.5\mspace{14mu} \log \; \log \; B} + {4C} + 2.34}{\log \; B}.$

This case is useful for a range of snr, where

exceeds a multiple of √{square root over (log B)} yet remains small compared to log B.

The second case has some leveling with 0<δ_(c)<snr. In this case the typical shape of the function g(x)−x, for x in [0, 1], is that it undergoes a single oscillation, first going down, then increasing, and then decreasing again, so there are two potential minima for x in [0, x*], one of which is at x*. In solving for the best rate drop bound, a role is demonstrated for the case that δ_(c) is such that an equal gap value is reached at these two minima. In this case, with optimized x*, a bound on Δ_(shape) is shown, for a range of intermediate size signal to noise ratios, to be given by the expression

${{\frac{2}{v\; \log \; B}\left\{ {2 + {\log \left( {\frac{1}{2} + \frac{v\; }{4C\sqrt{2\pi}}} \right)}} \right\}} + \frac{1}{2\mspace{14mu} \log \; B}},$

where

=snr/(1+snr). When 2

/

is small compared to τ/√{square root over (2π)}, this Δ_(shape) is near (1/

)(log log B)/(log B). When added to Δ_(alarm) it provides an expression for Δ_(B), as previously given, that is near (1.5+1/

)log log B/log B plus terms that are small in comparison.

The above expression provides the Δ_(shape) as long as snr is not too small and 2

/

is less than τ/√{square root over (2π)}. For 2

/

at least τ/=√{square root over (2π)}, the effect of the log log B is canceled, though there is then an additional small remainder term that is required to be added to the above as detailed later. The result is that Δ_(shape) is less than const/log B for (2

/

)√{square root over (2π)} at least τ.

The third case uses constant power allocation (complete leveling with δ_(c)=snr), when snr is small. The Δ_(shape) is less than a given bound near √{square root over (2(log log B)/log B)} when the snr is less than twice that value. For such sufficiently small snr this Δ_(shape) with complete leveling becomes superior to the expression given above for partial leveling.

Accordingly, let Δ_(shape) be the best of these values from the three cases, producing a continuous decreasing function of snr, near √{square root over (2(log log B)/log B)} for small snr, near (1+1/snr)log log B/(log B) for intermediate snr and near ½ log B for large snr. Likewise, the Δ_(B) bound is Δ_(shape)+Δ_(alarm). In this way one has the dependence of the rate drop on snr and section size B.

Thus let

_(B) be the rate of the composite sparse superposition inner code and Reed-Solomon outer code obtained from optimizing the total relative rate drop bound Δ_(B).

Included in Δ_(alarm) and Δ_(shape), which sum to Δ_(B), are baseline values of the false alarm rates and the failed detection rates, respectively, which add to provide a baseline value δ_(mis)*, and, accordingly, Δ_(B) splits as δ_(mis)* plus Δ_(B,inner), using the relative rate drop of the inner code. As detailed later, this δ_(mis)* is typically small compared to the rate drop sources from the inner code.

In putting the ingredients together, when R is less than

_(B), part of the difference

_(B)−R is used in providing slight increase past the baseline to determine a reliable δ_(mis), and the rest of the difference is used in setting the inner code rate to insure a sufficiently positive gap g(x)−x for reliability of the decoding progression. The relative choices are made to produce the best resulting error exponent Lε(

_(B)−R) for the given rate.

1.9 Comparison to Least Squares:

It is appropriate to compare the rate achieved here by the practical decoder herein with what is achieved with theoretically optimal, but possibly impractical, least squares decoding of these sparse superposition codes, subject to the constraint that there is one non-zero coefficients in each section. Such least squares decoding provides the stochastically smallest distribution of the number of mistakes, with a uniform distribution on the possible messages, but it has an unknown computation time.

In this direction, the results in the previously cited companion paper for least squares decoding of superposition codes, partially complement what is give herein for the adaptive successive decoder. For optimum least square decoding, favorable properties are demonstrated, in the case that the power assignments P/L are the same for each section. Interestingly, the analysis techniques there are different and do not reveal rate improvement from the use of variable instead of constant power with optimal least squares decoding. Another difference is that while here there are no restrictions on B, there it is required that B≧L^(b) for a specified section size rate b depending only on the signal-to-noise ratio, where conveniently b tends to 1 for large signal-to-noise, but unfortunately b gets large for small snr. For comparison with the scheme here, restrict attention to moderate and large signal-to-noise ratios, as for computational reasons, it is desirable that B be not more than a low order polynomial in L.

Let Δ=(

−R)/

be the rate drop from capacity, with R not more than

. For least squares decoding there is a positive constant c₁ such that the probability of more than a fraction δ_(mis) of mistakes is less than exp{nc₁ min{Δ², δ_(mis)}}, for any δ_(mis) in [0,1], any positive rate drop Δ and any size n. This bound is better than obtained for the practical decoder herein in its freedom of any choice of mistake fraction and rate drop in obtaining this reliability. In particular, the result for least squares does not restrict Δ to be larger than Δ_(B) and does not restrict δ_(mis) to be larger than a baseline value of order 1/log B.

It shows that n only needs to be of size [log(1/ε)]/[c₁ min{Δ², δ_(mis)}] for least squares to achieve probability ε of at least a fraction δ_(mis) mistakes, at rate that is Δ close to capacity. With suitable target fractions of mistakes, the drop from capacity Δ is not more than √{square root over ((1/c₁n)log 1/ε)}. It is of order 1/√{square root over (π)} if ε is fixed; whereas, for ε exponentially small in n, the associated drop from capacity Δ would need to be at least a constant amount.

An appropriate domain for comparison is in a regime between the extremes of fixed probability ε and a probability exponentially small in n. The probability of error is made nearly exponentially small if the rate is permitted to slowly approach capacity. In particular, suppose B is equal to n or a small order power of n. Pick Δ of order 1/log B to within iterated log factors, arranged such that the rate drop Δ exceeds the envelope Δ_(B) by an amount of that order 1/log B. One can ask, for a rate drop of that moderately small size, how would the error probability of least squares and the practical method compare? At a suitable mistake rate, the exponent of the error probability of least squares would be quantified by n/(log B)² of order n/(log n)², neglecting log log factors. Whereas, for the practical decoder herein the exponent would be a constant times L(Δ−Δ_(B))²(log B)^(1/2), which is of order L/(log B)^(1.5), that is, n/(log n)^(2.5). This the exponent for the practical decoder is within a (log n)^(0.5) factor of what is obtained for optimal least squares decoding.

1.10 Comparison to the Optimal Form of Exponents:

It is natural to compare the rate, reliability, and code-size tradeoff that is achieved here, by a practical scheme, with what is known to be theoretically best possible. What is known concerning the optimal probability of error, established by Shannon and Gallager, as reviewed for instance in work of Polyanskiy, Poor and Verdú (IEEE IT 2010), is that the optimal probability of error is exponentially small in an expression nε(R) which, for R near

, matches nΔ² to within a factor bounded by a constant, where Δ=(

−R)/

. As recently refined in the work of Altug and Wagner (IEEE ISIT 2010), this behavior of the exponent remains valid for Δ down to the order remaining larger than 1/√{square root over (n)}. The reason for that restriction is that for Δ as small as order 1/√{square root over (n)}, the optimal probability of error does not go to zero with increasing block length (rather it is then governed by an analogous expression involving the tail probability of the Gaussian distribution). It is reiterated that these optimal exponents are associated with analyses which provided no practical scheme to achieve them in the literature.

The bounds for the practical decoder do not rely on asymptotics, but rather finite sample bounds available for all choices of L and B and inner code rates R≦

_(B), with blocklength n=(L log B)/R. As derived herein the overall error probability bound is exponentially small in an expression of the form L mill {Δ, Δ²√{square root over (log B)}}, provided R is enough less than

_(B) that the additional drop from

_(B)−R is of the same order as the total drop Δ. Consequently, the error probability is exponentially small in

$n\; \min {\left\{ {\frac{\Delta}{\log \; B},\frac{\Delta^{2}}{\sqrt{\log \; B}}} \right\}.}$

Focussing on the Δ for which the square term is the minimizer, it shows that the error probability is exponentially small in n(

−R)²/√{square root over (log B)}, within a √{square root over (log B)} factor of optimal, for rates R for which

_(B)−R is of order between log log B/log B and 1/√{square root over (log B)}.

An alternative perspective on the rate and reliability tradeoff as in Polyanskiy, Poor, and Verdú, is to set a small block error probability e and seek the largest possible communication rate R_(opt) as a function of the codelength. They show for n of at least moderate size, this optimal rate is near

${R_{opt} = {C - {\frac{\sqrt{V}}{\sqrt{n}}\sqrt{2\mspace{14mu} \log \mspace{14mu} {1/\varepsilon}}}}},$

for a constant V they identify, where if ε is not small the √{square root over (2 log 1/ε)} is to be replaced by the upper ε quantile of the standard normal. For small ε this expression agrees with the form of the relationship between error probability c and the exponent n(

−R_(opt))² stated above. The rates and error probabilities achieve with the practical decoder herein have a similar form of relationship but differ in three respects. One is that here there is the somewhat smaller n/√{square root over (log B)} in place of n, secondly the constant multipliers do not match the optimal V, and thirdly the result herein is only applicable for ε small enough that the rate drop is made to be at least Δ_(B).

From either of these perspectives, the results here show that to gain provable practicality a price is paid of needing blocklength larger by a factor of √{square root over (log B)} to have the same performance as would be optimal without concern for practicality.

1.11 On the Signal Alphabet and Shaping:

From the cited review by Formey and Ungerboeck, as previously said, the problem of practical communication for additive Gaussian noise channels, has been decomposed into separate problems, which in addition to modulation, include the matters of choice of signal alphabet, of the shaping of a signal constellation, and of coding. The approach taken herein merges the signal alphabet and constellation into the coding. The values of codeword symbols that arise in herein are those that can be realized via sums of columns of the dictionary, one from each section in the partitioned case. Some background on signalling facilitates discussion of relationship to other work.

By choice of signal alphabet, codes for discrete channels have been adapted to use on Gaussian channels, with varying degrees of success. In the simplest case the code symbols take on only two possible values, leading to a binary input channel, by constraining the symbol alphabet to allow only the values ±√{square root over (P)} and possibly using only the signs of the Y_(i). With such binary signalling, the available capacity is not more than 1 and it is considerably less than (½)log(1+snr), except in the case of low signal-to-noise ratio. When considering snr that is not small it is preferable to not restrict to binary signalling, to allow higher rates of communication. When using signals where each symbol has a number M of levels, the rate caps at log M, which is achieved in the high snr limit even without coding (simply infer for each symbol the level to which the received Y_(i) is closest). As quantified in Formey and Ungerboeck, for moderate snr, treating the channel as a discrete M-ary channel of particular cross-over probabilities and considering associated error-correcting codes allows, in theory, for reasonable performance provided log M sufficiently exceeds log snr (and empirically good coding performance has been realized by LDPC and turbo codes). Nevertheless, as they discuss, the rate of such discrete channels with a fixed number of levels remains less than the capacity of the original Gaussian channel.

To bring the rate up to capacity, the codeword choices must be properly shaped, that is, the codeword vectors should approximate a good packing of points on the n-dimensional sphere of squared radius dictated by the power. An implication of which is that, marginally and jointly for any subset of codeword coordinates, the set of codewords should have empirical distribution not far from Gaussian. Such shaping is likewise a problem for which theory dictates what is possible in terms of rate and reliability, but theory has been lacking to demonstrate whether there is a moderate or low complexity of decoding that achieves such favorable rate and error probability.

The sparse superposition code automatically takes care of the required shaping of the multivariate signal constellation by using linear combinations of subsets a given set of real-valued Gaussian distributed vectors. For high snr; the role of log M being large compared to log snr is replaced herein by having L large and having log B large compared to

. These sparse superposition codes are not exactly well-spaced on the surface of an n-sphere, as inputs that agree in most sections would have nearby codewords. Nevertheless, when coupled with the Reed-Solomon outer code, sufficient distance between codewords is achieved for quantifiably high reliability.

1.12 Relationships to Previous Work:

Several directions of past work are discussed that connect to what is developed here. There is some prior work concerning computational feasibility for reliable communications near capacity for certain channels. Building on Gallager's low density parity check codes (LDPC), iterative decoding algorithms based on statistical belief propagation in loopy networks have been empirically shown in various works to provide reliable and moderately fast decoding at rates near the capacity for various discrete channels, and mathematically proven to provide such properties only in the special case of the binary erasure channel in Luby, Mizemnacher, Shokrollahi, and Spielman (IEEE IT 2001). Belief networks are also used for certain empirically good schemes such as turbo codes that allow real-valued received symbols, with a discrete set of input levels. Though substantial steps have been made in the analysis of such belief networks, as summarized for instance in the book by Richardson and Urbanke (2008), there is not proof of the desired properties at rates near capacity for the Gaussian channel.

When both schemes are set up with rate near capacity, the critical distinction between empirically demonstrated code performance (as in the case of LDPC and turbo codes), and a quantified exponential scaling of error probability (as with sparse superposition code with adaptive successive decoder) is what is asserted herein by the exponential scaling of error probability. With such error rate scaling, as the size of the code doubles while maintaining the same communication rate R, then the error probability is squared. For instance an error probability of 10⁻⁴ then reduces to 10⁻⁸, likewise multiplying the code size by 4 would reduce the error probability to 10⁻¹⁶.

In the progression of available computational ability, such doubling of the size of memory allocatable to the same size chip, is a customary matter in computer chip technology that has occurred every couple of years. With the code invented herein one knows that the reliability will scale in such an attractive way as computational resources improve. With LDPC and turbo codes one might guess that the error probability will likewise improve, but with those technologies (and all other existing code technologies) it can not be definitively asserted. Empirical simulation can not come to the rescue when planning for several years ahead. Until there are the computational resources to implement such increased size devices one can not know whether the investment in existing code strategies (other than ours) will be rewarded when they are increased in size.

An approach to reliable and computationally-feasible decoding, with restriction to binary signaling, is in the work on channel polarization of Arikan (IEEE IT 2009) and Arikan and Telatar (IEEE IT 2010). Error probability is demonstrated there at a level exponentially small in n^(1/2) for fixed rates less than the binary signaling capacity. In contrast for the scheme herein, the error probability is exponentially small in n to within a logarithmic factor and communication is permitted at higher rates than would be achieved with binary signalling, approaching capacity for the Gaussian noise channel. In recent work Emmanuel Abbe adapts channel polarization to achieve the sum rate capacity for m user binary input multiple-access channels, with specialization to single-user channels with 2^(m) inputs. Building on that work. Abbe and Barron (IEEE ISIT 2011) are investigating discrete near-Gaussian signalling to adapt channel polarization to the Gaussian noise channel. That provides an alternative interesting approach to achieving rates up to capacity for the Gaussian noise, but not with error exponents exponentially small in n.

The analysis of concatenated codes in the book of Formey (1966) is an important fore-runner to the development of code composition given herein. For the theory, he paired an outer Reed-Solomon code with concatenation of optimal inner codes of Shannon-Gallager type, while, for practice he focussed on binary input channels, he paired such an outer Reed-Solomon code with inner based on linear combinations of orthogonal terms (for target rates Kin less than 1 such a basis is available), in which all binary coefficient sequences are possible codewords.

A challenge concerning theoretically good inner codes is that the number of messages searched is exponentially large in the inner codelength. Formey made the inner codelength of logarithmic size compared to the outer codelength as a step toward practical solution. However, caution is required with these strategies. Suppose the rate of the inner code has a small relative drop from capacity, Δ=(

−R)/

. For at least moderate reliability, the inner codelength would be of order at least 1/Δ². So with these the required outer codelength becomes exponential in 1/Δ².

To compare, for the Gaussian noise channel, the approach herein provides a practical decoding scheme for the inner code. Herein inner and outer codelengths are permitted that are comparable to each other. One can draw a parallel between the sections described here and the concatenations of Formey's inner codes. However, a key difference is use herein of superposition across the sections and the simultaneous decoding of these sections. Challenges remain in the restrictiveness of the relationship of the rate drop Δ to the section sizes. Nevertheless, particular rates are identified as practical and near optimal.

By having set up the channel coding via the linear model Y=Xβ+ε with a sparse coefficient vector β, it is appropriate to discuss the relationships of the iterative communication decoder here with other iterative algorithms for statistical signal recovery. Though some similarities are here-below described, an important distinction is that previously obtained constrained least squares coefficient estimators have not been developed for communication at rates near capacity for the Gaussian channel.

A class of algorithms for seeking fits of the form Xβ to an observed response vector Y are those designed for the task of finding the least squares convex projection. This projection can either be to the convex hull of the columns of the dictionary X or, for the present problem, to the convex hull of the sums of columns, one from each section in the partitioned case. The relaxed greedy algorithm is an iterative procedure that solves such problems, applying previous theory by Lee Jones (1992), Barron (1993), Lee, Bartlett and Williamson (1996), or Barron, Cohen, et all (2007). Each pass of the relaxed greedy algorithm is analogous to the steps of decoding algorithm developed herein, though with an important distinction. This convex projection algorithm finds in each section the term of highest inner product with the residuals from the previous iteration and then uses it to update the convex combination. Accordingly, like the adaptive decoder here, this algorithm has computation resource requirements linear in the product of the size of the dictionary and the number iterations. The cited theory bounds the accuracy of the fit to the projection as a function of the number of iterations.

The distinction is that convex projection seeks convex combinations of vertices, whereas the decoding problem here can be regarded as seeking the best vertex (or a vertex that agrees with it in most sections). Both algorithms embody a type of relaxation. The Jones-style relaxation is via the convex combination down-weighting the previous fits. The adaptive successive decoder instead achieves relaxation by leaving sections un-decoded on a step if the inner product is not yet above threshold. At any given step the section fit is restricted to be a vertex or 0.

The inventors have conducted additional analysis of convex projection in the case of equal power allocation in each section. An approximation to the projection can be characterized which has largest weight in most sections at the term sent, when the rate R is less than R₀; whereas for larger rates the weights of the projection are too spread across the terms in the sections to identify what was sent. To get to the higher rates, up to capacity, one cannot use such convex projection alone. Variable section power may be necessary in the context of such algorithms. It advantageous to conduct a more structured iterative decoding, which is more explicitly targeted to finding vertices, as presented here.

The conclusions concerning communication rate may also be expressed in the language of sparse signal recovery and compressed sensing. A number of terms selected from a dictionary is linearly combined and subject to noise. Suppose a value B is specified for the ratio of the number of variables divided by the number of terms. For signals of the form Xβ with β satisfying the design stipulations used here. Recovery of these terms from the received noisy Y of length n is possible provided the number of terms L satisfies L≧Rn/log B. Equivalently the number of observations sufficient to determine L terms satisfies n≦(1/R)L log B. In this signal recovery story, the factor 1/R is not arising as reciprocal of rate, but rather as the constant multiplying L log N/L determining the sample size requirement.

Our results interpreted in this context show for practical recovery that there is the R<R₀ limitation in the equal power allocation case. For other power allocations designed here, recovery by other means is possible at higher R up to the capacity

. Thus our practical solution to the communications capacity problem provides also practical solution to the analogous signal recovery problem as well as demonstration of the best constant for signal recovery for a certain behavior of the non-zero coefficients.

These conclusions complement work on sparse signal recovery by Wainwright (IEEE IT 2009a,b), Fletcher, Rangan, Goyal (IEEE IT 2009), Donoho, Elad, Temlyakov (IEEE IT 2006), Candes and Palm (Ann. Statist. 2009), Tropp (IEEE IT 2006), and Tong Zhang. In summary, their work shows that for reliable determination of L terms from noisy measurements, having the number of such measurements n be of order L log B is sufficient, and is achieved by various estimator (including convex optimization with an l_(l) control on the coefficients as in Wainwright and a forward stepwise regression algorithm in Zhang analogous to the greedy algorithms discussed above). There results for signal recovery, when translated into the setting of communications, yield reliable communications with positive rate, by not allowance for rates up to capacity. Wainwright (IEEE IT 2009a,b) makes repeated use of information-theoretic techniques including the connection with channel coding, allowing use of Fano's inequality to give converse-like bounds on sparse signal recovery. His work shows, for the designs he permits, that l_(l) constrained convex optimization does not perform as well as the information-theoretic limits. As said above, the work herein takes it further, identifying the rates achieved in the constant power allocation case, as well as identifying practical strategies that do achieve up to the information-theoretic capacity, for specify variable power allocations.

The ideas of superposition codes, rate splitting, and successive decoding for Gaussian noise channels began with Cover (IEEE IT 1972) in the context of multiple-user channels. In that setting what is sent is a sum of codewords, one for each message. Instead the inventors herein are putting that idea to use for the original Shannon single-user problem. The purpose here of computational feasibility is different from the original multi-user purpose which was characterization of the set of achievable rates. The ideas of rate splitting and successive decoding originating in Cover for Gaussian broadcast channels were later developed also for Gaussian multiple-access channels, where in the rate region characterizations of Rimoldi and Urbanke (IEEE IT 2001) and of Cao and Yeh (IEEE IT 2007) rate splitting is in some cases applied to individual users. For instance with equal size rate splits there are 2^(nR/L) choices of code pieces, corresponding to the sections of the dictionary as used here.

So the applicability of superposition of rate split codes for a single user channel has been noted, albeit the rate splitting in the traditional information theory designs have exponential size 2^(nR/L) to gain reliability. Feasibility for such channels has been lacking in the absence of demonstration of reliability at high rate with superpositions from polynomial size dictionaries. In contrast success herein is built on the use of sufficiently many pieces (sections) with L of order n to within log factors such the section sizes B=2^(nR/L) become moderate (e.g. also of order n). Now with such moderate B one can not assure reliability of direct successive decoding. As said, to overcome that difficulty, adaptation rather than pre-specification of the set of sections decoded each step is key to the reliability and speed of the decoder invented here.

It is an attractive feature of the superposition based solution obtained herein for the single-user channel that it is amenable to extension to practical solution of the corresponding multi-user channels, namely, the Gaussian multiple access and Gaussian broadcast channel.

Accordingly, the invention is understood to include those aspects of practical solution of Gaussian noise broadcast channels and multiple-access channels that arise directly from combining the single-user style adaptive successive decoder analysis here with the traditional multi-user rate splitting steps.

Outline of Manuscript:

After some preliminaries, section 3 describes the decoder. In Section 4 the distributions of the various test statistics associated with the decoder are analyzed. In particular, the inner product test statistics are shown to decompose into normal random variables plus a nearly constant random shift for the terms sent. Section 5 demonstrates the increase for each step of the mean separation between the statistics for terms sent and terms not sent. Section 6 sets target detection and alarm rates. Reliability of the algorithm is established in section 7, with demonstration of exponentially small error probabilities. Computational illustration is provided in section 8. A requirement of the theory is that the decoder satisfies a property of accumulation of correct detections. Whether the decoder is accumulative depends on the rate and the power allocation scheme. Specialization of the theory to a particular variable power allocation scheme is presented in section 9. The closeness to capacity is evaluated in section 10. Lower bounds on the error exponent are in section 11. Refinements of closeness to capacity are in section 12. Section 13 discusses the use of an outer Reed-Solomon code to correct any mistakes from the inner decoder. The appendix collects some auxiliary matters.

2 Some Preliminaries

Notation:

For vectors a, b of length n, let ∥a∥² be the sum_of squares of coordinates, let |a|²=(1/n)Σ_(i=1) ^(n)a_(i) ² be the average square and let respectively a^(T)b and a·b=(1/n)Σ_(i=1) ^(n)a_(i)b_(i) be the associated inner products. It is more convenient to work with |a| and a·b.

Setting of Analysis:

The dictionary is randomly generated. For the purpose of analysis of average probability of error or average probability of at least certain fraction of mistakes, properties are investigated with respect to the joint distribution of the dictionary and the noise.

The noise ε and the X_(j) in the dictionary are jointly independent normal random vectors, each of length n, with mean equal to the zero vector and covariance matrixes equal to σ²I and I, respectively. These vectors have n coordinates indexed by i=1, 2, . . . , n which may be called the time index. Meanwhile J is the set of term indices j corresponding to the columns of the dictionary, which may be organized as a union of sections. The codeword sent is from a selection of L terms. The cardinality of J is N and the ratio B=N/L.

Corresponding to an input, let sent={j₁,j₂, . . . , j_(L)} be the indices of the terms sent and let other=J−sent be the set of indices of all other terms in the dictionary. Component powers P_(j) are specified, such that Σ_(j sent)P_(j)=P. The simplest setting is to arrange these component powers to be equal P_(j)=P/L. Though for best performance, there will be a role for component powers that are different in different portions of the dictionary. The coefficients for the codeword sent are β_(j)=√{square root over (P_(j))}1_(j sent). The received vector is

$Y = {{\sum\limits_{j}^{\;}\; {\beta_{j}X_{j}}} + {ɛ.}}$

Accordingly, X_(j) and Y are joint normal random vectors, with expected product between coordinates and hence expected inner product

[X_(j)·Y] equal to β_(j). This expected inner product has magnitude √{square root over (P_(j))} for the terms sent and 0 for the terms not sent. So the statistics X_(j)·Y are a source of discrimination between the terms.

Note that each coordinate of Y has expected square σ_(Y) ²=P+σ² and hence

[|Y|²]=P+σ².

Exponential Bounds for Relative Frequencies:

In the distributional analysis repeated use is made of simple large deviations inequalities. In particular, if {circumflex over (q)} is the relative frequency of occurrence of L independent events with success probability q*, then for q<q* the probability of the event {{circumflex over (q)}<q} is not more than the Sanov-Csiszàr bound e^(−LD(q∥q*)), where the exponent D(q∥q*)=D_(Ber)(q∥q*) is the relative entropy between Bernoulli distributions. This information-theoretic bound subsumes the Hoeffding bound e^(−2(q*−q)) ² ^(L) via the Csiszàr-Kullback inequality that D exceeds twice the square of total variation, which here is, D≧2(q*−q)². An extension of the information-theoretic bound to cover weighted combinations of indicators of independent events is in Lemma 46 in the appendix and slight dependence among the events is addressed through bounds on the joint distribution. The role of {circumflex over (q)} is played by weighted counts for j in sent of test statistics being above threshold.

In the same manner, one has that if {circumflex over (p)} is the relative frequency of occurrence of independent events with success probability p*, then for p>p* the probability of the event {{circumflex over (p)}>p} has a large deviation bound with exponent D_(Ber)(p∥p*). In the use here of such bounds, the role of {circumflex over (p)} is played by the relative frequency of false alarms, based on occurrences of j in other of test statistics being above threshold. Naturally, in this case, both p and p* are arranged to be small, with some control on the ratio between them. It is convenient to make use of lower bounds on D_(Ber)(p∥p*), as detailed in Lemma 47 in the appendix, which include what may be called the Poisson bound p log p/p*+p*−p and the Bellinger bound 2(√{square root over (p)}−√{square root over (p*)})², both of which exceed (p−p*)²/(2p). All three of these lower bounds are superior to the variation bound 2(p−p*)² when p is small.

3 The Decoder

From the received Y and knowledge of the dictionary, decode which terms were sent by an iterative procedure now specified more fully.

The first step is as follows. For each term X_(j) of the dictionary compute the inner product with the received string X_(j) ^(T)Y as a test statistic and see if it exceeds a threshold T=∥Y∥_(τ). Denote the associated event

_(j) ={X _(j) ^(T) Y≧T}.

In terms of a normalized test statistic this first step test is the same as comparing

_(1,j) to a threshold τ, where

_(1,j) =X _(j) ^(T) Y/∥Y∥,

the distribution of which will be shown to be that of a standard normal plus a shift by a nearly constant amount, where the presence of the shift depends on whether j is one of the terms sent. Thus

_(j)={

_(1,j)≧τ}. The threshold is chosen to be

τ=√{square root over (2 log B)}+a.

The idea of the threshold on the first step is that very few of the terms not sent will be above threshold. Yet a positive fraction of the terms sent, determined by the size of the shift, will be above threshold and hence will be correctly decoded on this first step.

Let thresh₁={jεJ:

=1} be the set of terms with the test statistic above threshold and let above₁ denote the fraction of such terms. In the variable power case it is a weighted fraction above₁=Σ_(jεthresh) ₁ P_(j)/P, weighted by the power P_(j). The strategy is to restrict decoding on the first step to terms in thresh₁ so as to avoid false alarms. The decoded set is either taken to be dec₁=thresh₁ or, more generally, a value pace₁ is specified and, considering the terms in J in order of decreasing

_(1,j), include in dec₁ as many as can be with Σ_(jεdec) ₁ π_(j) not more than min{pace₁, above₁}. Let DEC₁ denote the cardinality of the set dec₁.

The output of the first step consists of the set of decoded terms dec₁ and the vector F₁=Σ_(jεdec) ₁ √{square root over (P_(j))}X_(j) which forms the first part of the fit. The set of terms investigated in step 1 is J₁=J, the set of all columns of the dictionary. Then the set J₂=J₁−dec₁ remains for second step consideration. In the extremely unlikely event that DEC₁ is already at least L there will be no need for the second step.

A natural way to conduct subsequent steps would be as follows. For the second step compute the residual vector

r ₂ =Y−F ₁.

For each of the remaining terms, i.e. terms in J₂, compute the inner product with the vector of residuals, that is, X_(j) ^(T)r₂ or its normalized form

_(j) ^(r)−X_(j) ^(T)r₂/∥r₂∥ which may be compared to the same threshold τ=√{square root over (2 log B)}+a, leading to a set dec₂ of decoded terms for the second step. Then compute F₂=Σ_(jεdec) ₂ √{square root over (P_(j))}X_(j), the fit vector for the second step.

The third and subsequent steps would proceed in the same manner as the second step. For any step k, one computes the residual vector

r _(k) =Y−(F ₁ + . . . +F _(k−1)).

For terms in J_(k)=J_(k−1)−dec_(k−1), one gets thresh_(k) as the set of terms for which X_(j) ^(T)r_(k)/∥r_(k)∥ is above τ. The set of decoded terms is either taken to be thresh_(k) or a subset of it. The decoding stops when the size of the cardinality of the set of all decoded term becomes L or there are no terms above threshold in a particular step. 3.1 Statistics from Adaptive Orthogonal Components:

A variant of the above algorithm from second step onwards is described, which is found here to be easier to analyze. The idea is that the ingredients Y, F₁, . . . , F_(k−1) previously used in forming the residuals may be decomposed into orthogonal components and test statistics formed that entail the best combinations of inner products with these components.

In particular, for the second step the vector G₂ is formed, which is the part of F₁ orthogonal to G₁=Y. For j in J₂, the statistic

_(2,j)=X_(j) ^(T)G₂/∥G₂∥ is computed as well as the combined statistic

_(2,j) ^(comb)=√{square root over (λ₁)}

_(1,j)−√{square root over (λ₂)}

_(2,j), where λ₁=1−λ and λ₂=λ, with a value of λ to be specified. What is different on the second step is that now the events

_(2,j)={

_(2,j) ^(comb)≧τ} are based on these

_(2,j) ^(comb), which are inner products of X_(j) with the normalized vector E₂=√{square root over (λ₁)}Y/∥Y∥−√{square root over (λ₂)}G₂/∥G₂∥. To motivate these statistics note the residuals r₂=Y−F₁ may be written as (1−{circumflex over (b)}₁)Y−G₂ where {circumflex over (b)}₁=F₁ ^(T)Y/∥Y∥². The statistic used in this variant may be viewed as approximations to the corresponding statistics based on the normalized residuals r₂/∥r₂∥, except that the form of λ and the analysis are simplified.

Again these test statistics

hd 2,j^(comb) lead to the set thresh₂={jεJ₂:

=1} of size above₂=Σ_(jεthresh) ₂ π_(j). Considering these statistics in order of decreasing value, it leads to the set dec₂ consisting of as many of these as can be while maintaining accept₂≦min{pace₂, above₂}, where accept₂=Σ_(jεdec) ₂ π_(j). This provides an additional part of the fit F₂=Σ_(jεdec) ₂ √{square root over (P_(j))}X_(j).

Proceed in this manner, iteratively, to perform the following loop of calculations, for k≧2. From the output of step k−1, there is available the vector F_(k−1), which is a part of the fit, and for k′<k there are previously stored vectors G_(k′) and statistics

_(k′,j). Plus there is a set dec_(1,k−1)=dec₁∪ . . . ∪ dec_(k−1) already decoded on some previous step and a set J_(k)=J−dec_(1,k−1) of terms for is to test at step k. Consider, as discussed further below, the part G_(k) of F_(k−1) orthogonal to the previous G_(k′) and for each j not in dec_(k−1) compute

_(k,j) =X _(j) ^(T) G _(k) /∥G _(k)∥

and the combined statistic

_(k,j) ^(comb)=√{square root over (λ_(1,k))}

_(1,j)−√{square root over (λ_(2,k))}

_(2,j)− . . . −√{square root over (λ_(k,k))}

_(k,j),

where these λ will be specified with Σ_(k′=1) ^(k)λ_(k′,k)=1. These positive weights will take the form λ_(k′,k)=w_(k′)/s_(k), with w₁=1, and s_(k)=1+w₂+ . . . w_(k), with w_(k) to be specified. Accordingly, the combined statistic may be computed by the update comb

_(k,j) ^(comb)=√{square root over (1−λ_(k))}

_(k−1,j) ^(comb)−√{square root over (λ_(k))}

_(k,j),

where λ_(k)=w_(k)/s_(k). This statistic may be thought of as the inner product of X_(j) with a vector updated as E_(k)=√{square root over (1−λ_(k))}E_(k−1)−√{square root over (λ_(k))}G_(k)/∥G_(k)∥, serving as a surrogate for r_(k)/∥r_(k)∥. For terms j in J_(k) these statistics

_(k,j) ^(comb) are compared to a threshold, leading to the events

_(k,j)={

_(k,j) ^(comb)≧τ}.

The idea of these steps is that, as quantified by an analysis of the distribution of the statistics

_(k,j), there is an increasing separation between the distribution for terms j sent and the others.

Let thresh_(k)={jεJ_(k):

_(k,j) ^(comb)≧τ_(k)} and above_(k)=Σ_(jεthresh) _(k)π_(j) and for a specified pace_(k), considering these test statistics in order of decreasing value, include in dec_(k) as many as can be with accept_(k)≦min{pace_(k), above_(k)}, where accept_(k)=Σ_(jεdec) _(k) π_(j). The output of step k is the vector

$F_{k} = {\sum\limits_{j \in {dec}_{k}}^{\;}{\sqrt{P_{j}}{X_{j}.}}}$

Also the vector G_(k) and the statistics

_(k,j) are appended to what was previously stored, for all terms not in the decoded set. From this step update is provided to the set of decoded terms dec_(1,k)=dec_(k−1)∪dec_(k) and the set J_(k+1)=J_(k)−dec_(k) of terms remaining for consideration.

This completes the actions of step k of the loop.

To complete the description of the decoder, the values of w_(k) that determine the λ_(k) will need to be specified and likewise pace_(k) is to be specify. For these specifications there will be a role for measures of the accumulated size of the detection set accept_(k) ^(tot)=Σ_(k′=1) ^(k) accept_(k′) as well a target lower bound q_(1,k) on the total weighted fraction of correct detection (the definition of which arises in a later section), and an adjustment to it given by q_(1,k) ^(adj)=q_(1,k)/(1+f_(1,k)/q_(1,k)) where f_(1,k) is a target upper bound on the total weighted fraction of false alarms. The choices considered here take w_(k)=s_(k)−s_(k−1) to be increments of the sequence s_(k)=1/(1−x_(k−1)ν) that arises in characterizing the above mentioned separation. In the definition of w_(k) the x_(k−1) is taken to be as either accept_(k−1) ^(tot) or q_(1,k−1) ^(adj), both of which arise as surrogates to a corresponding unobservable quantity which would require knowledge of the actual fraction of correct detection through step k−1.

There are two options for pace_(k) that are described. First, one may arrange for dec_(k) to be all of thresh_(k) by setting pace_(k)=1, large enough that it has essentially no role, and with this option the w_(k) is set as above using x_(k−1)=accept_(k−1) ^(tot). This choice yields a successful growth of the total weighted fractions of correct detections, though to handle the empirical character of w_(k) there is a slight cost to it in the reliability bound, not present with the second option.

For the second option, let pace_(k)=g_(1,k) ^(adj)−q_(1,k−1) ^(adj) be the deterministic increments of the increasing sequence q_(1,k) ^(adj), with which it is shown that above_(k) is likely to exceed pace_(k), for each k. When it does then accept_(k) equals the value pace_(k), and cumulatively their sum accept_(k) ^(tot) matches the target q_(1,k) ^(adj). Likewise, for this option, w_(k), is set using x_(k−1)=q_(1,k−1) ^(adj). It's deterministic trajectory facilitates the demonstration of reliability of the decoder.

On each step k the decoder uncovers a substantial part of what remains, because of growth of the mean separation between terms sent and the others, as shall be seen.

The algorithm stops under the following conditions. Natural practical conditions are that L terms have been decoded, or that the weighted total size of the decoded set accept_(k) ^(tot) has reached at least 1, or that no terms from J_(k) are found to have statistic above threshold, so that F_(k) is zero and the statistics would remain thereafter unchanged. An analytical condition is the lower bound that will be obtained on the likely mean separation stops growing (captured through q_(1,k) ^(adj) no longer increasing), so that no further improvement is theoretically demonstrable by such methodology. Subject to rate constraints near capacity, the best bounds obtained here occur with a total number of steps m equal to an integer part of 2+snr log B.

Up to step k, the total set of decoded terms is dec_(1,k), and the corresponding fit fit_(k) may be represented either as Σ_(jεdec) _(1,k) √{square root over (P_(j))}X_(j) or as the sum of the pieces from each step

fit _(k) =F ₁ +F ₂ + . . . +F _(k).

As to the part G_(k) of F_(k−1) orthogonal to G_(k′) for k′<k, take advantage of two ways to view it, one emphasizing computation and the other analysis.

For computation, work directly with parts of the fit. The G₁, G₂, . . . , G_(k−1) are orthogonal vectors, so the parts of F_(k−1) in these directions are {circumflex over (b)}_(k,k′)G_(k′) with coefficients {circumflex over (b)}_(k,k′)=F_(k−1) ^(T)G_(k′)/∥G_(k′)∥² for k′=1, 2, . . . , k−1, where if peculiarly ∥G/k′∥=0 use {circumflex over (b)}_(k,k′)=0. Accordingly, the new G_(k) may be computed from F_(k−1) and the previous G_(k′) with k′/<k by

$G_{k} = {F_{k - 1} - {\sum\limits_{k^{\prime} = 1}^{k - 1}\; {{\hat{b}}_{k,k^{\prime}}{G_{k^{\prime}}.}}}}$

This computation entails the nfold sums of products F_(k) ^(T)G_(k′) for determination of the {circumflex over (b)}_(k,k′). Then from this computed G_(k) obtain the inner products with the X_(j) to yield

_(k,j)=X_(j) ^(T)G_(k)/∥G_(k)∥ for j in J_(k).

The algorithm is seen to perform an adaptive Gram-Schmidt orthogonalization, creating orthogonal vectors G_(k) used in representation of the X_(j) and linear combinations of them, in directions suitable for extracting statistics of appropriate discriminatory power, starting from the received Y. For the classical Gram-Schmidt process, one has a pre-specified set of vectors which are successively orthogonalized, at each step, by finding the part of the current vector that is orthogonal to the previous vectors. Here instead, for each step, the vector F_(k−1), for which one finds the part G_(k) orthogonal to the vectors G₁, . . . , G_(k−1), is not pre-specified. Rather, it arises from thresholding statistics extracted in creating these vectors.

For analysis, look at what happens to the representation of the individual terms. Each term X_(j) for jεJ_(k−1) has the decomposition

${X_{j} = {{_{1,j}\frac{G_{1}}{G_{1}}} + {_{2,j}\frac{G_{2}}{G_{2}}} + \ldots + {_{{k - 1},j}\frac{G_{k - 1}}{G_{k - 1}}} + V_{k,j}}},$

where V_(k,j) is the part of X_(j) orthogonal to G₁, G₂, . . . , G_(k−1). Since

$F_{k - 1} = {\sum\limits_{j \in {dec}_{k - 1}}^{\;}{\sqrt{P_{j}}X_{j}}}$

it follows that G_(k) has the representation

${G_{k} = {\sum\limits_{j \in {dec}_{k - 1}}^{\;}\; {\sqrt{P_{j}}V_{k,j}}}},$

from which

_(k,j)=V_(k,j) ^(T)G_(k)/∥G_(k)∥, and one has the updated representation

$X_{j} = {{_{1,j}\frac{G_{1}}{G_{1}}} + \ldots + {_{{k - 1},j}\frac{G_{k - 1}}{G_{k - 1}}} + {_{k,j}\frac{G_{k}}{G_{k}}} + {V_{{k + 1},j}.}}$

With the initialization V_(0,j)=X_(j), these V_(k+1,j) may be thought of as iteratively obtained from the corresponding vectors at the previous step, that is,

V _(k+1,j) V _(k,j)−

_(k,j) G _(k) /∥G _(k)∥.

These V do not actually need to be computed, nor do its components detailed below, but this representation of the terms X_(j) is used in obtaining distributional properties of the

_(k,j).

3.2 The Weighted Fractions of Detections and Alarms:

The weights π_(j)=P_(j)/P sum to 1 across in sent and they sum to B−1 across j in other. Define in general

${\hat{q}}_{k} = {\sum\limits_{j \in {{sent}\bigcap{dec}_{k}}}^{\;}\pi_{j}}$

for the step k correct detections and

${\hat{f}}_{k} = {\sum\limits_{j \in {{other}\bigcap{dec}_{k}}}^{\;}\pi_{j}}$

for the false alarms. In the case P_(j)=P/L which assigns equal weight π_(j)=1/L, then {circumflex over (q)}_(k) L is the increment to the number of correct detections on step k, likewise {circumflex over (f)}_(k) L is the increment to the number of false alarms. Their sum accept_(k)={circumflex over (q)}_(k)+{circumflex over (f)}_(k) matches Σ_(jεdec) _(k) π_(j).

The total weighted fraction of correct detections up to step k is {circumflex over (q)}_(k) ^(tot)=Σ_(jεsent∩dec) _(1,k) π_(j) which may be written as the sum

{circumflex over (q)} _(k) ^(tot) ={circumflex over (q)} ₁ +{circumflex over (q)} ₂+ . . . +{circumflex over (q)}_(k).

Assume for now that dec_(k)=thresh_(k). Then these increments {circumflex over (q)}_(k) equal Σ_(jεsentΩJ) _(k) π_(j)

The decoder only encounters these

_(k,j)={

_(k,j) ^(comb)>τ} for j not decoded on previous steps, i.e., for j in J_(k)=(dec_(1,k−1))^(c). For each step k, one may define the statistics arbitrarily for j in dec_(1,k−1), so as to fill out definition of the events

_(k,j) for each j, in a manner convenient for analysis. By induction on k, on sees that dec_(1,k) consists of the terms j for which the union event

_(1,j)∪ . . . ∪

_(k,j) occurs. Because if dec_(1,k−1)={j:

=1} then the decoded set dec_(1,k) consists of terms for which either

_(1,j)∪ . . . ∪

_(k−1,j) occurs (previously decoded) or

_(k,j)∪[

_(1,j)∪ . . . ∪

_(k−1,j)]^(c) occurs (newly decoded), and together these events constitute the union

_(1,j)∪ . . . ∪

_(k,j).

Accordingly, the total weighted fraction of correct detection {circumflex over (q)}_(k) ^(tot) may be regarded as the same as the π weighted measure of the union

${\hat{q}}_{k}^{tot} = {\sum\limits_{j\mspace{14mu} {sent}}^{\;}{\pi_{j}{1_{\{{\mathcal{H}_{1,j}\bigcup\mspace{14mu} \ldots \mspace{14mu}\bigcup\mathcal{H}_{k,j}}\}}.}}}$

Indeed, to relate this expression to the preceding expression for the stun for {circumflex over (q)}_(k) ^(tot), the sum for k′ from 1 to k corresponds to the representation of the union as the disjoint union of contributions from terms sent that are in

_(k′,j) but not in earlier such events.

Likewise the weighted count of false alarms {circumflex over (f)}_(k) ^(tot)=Σ_(jεother∪dec) _(1,k) π_(j) may be written as

{circumflex over (f)} _(k) ^(tot) ={circumflex over (f)} ₁ +{circumflex over (f)} ₂+ . . . +{circumflex over (f)}_(k)

which when dec_(k)=thresh_(k) may be expressed as

${\hat{f}}_{k}^{tot} = {\sum\limits_{j\mspace{14mu} {other}}^{\;}{\pi_{j}{1_{\{{\mathcal{H}_{1,j}\bigcup\mspace{14mu} \ldots \mspace{14mu}\bigcup\mathcal{H}_{k,j}}\}}.}}}$

In the distributional analysis that follows the mean separation is shown to be given by an expression inversely related to 1−{circumflex over (q)}_(k−1) ^(tot)ν. The idea of the multi-step algorithm is to accumulate enough correct detections in {circumflex over (q)}_(k) ^(tot), with an attendant low number of false alarms, that the fraction that remains becomes small enough, and the mean separation hence pushed large enough, that most of what remains is reliably decoded on the last step.

The analysis will provide, for each section l, lower bounds on the probability that the correct term is above threshold by step k and upper-bounds on the accumulated false alarms. When the snr is low and a constant power allocation is used, these probabilities are the same across the sections, all of which remain active for consideration until completion of the steps.

For variable power allocation, with P_((l)) decreasing in l, then for each step k, the probability that the correct term is above threshold varies with l. Nevertheless, it can be a rather large number of sections for which this probability takes an intermediate value (neither small nor close to one), thereby necessitating the adaptive decoding. Most of the analysis here proceeds by allowing at each step for terms to be detected from any section l=1, 2, . . . , L.

3.3 An Optional Analysis Window:

For large C, the P_((l)) proportional to e^(−2Cl/L) exhibits a strong decay with increasing l. Then it can be appropriate to take advantage of a deterministic decomposition into three sets of sections at any given number of steps. There is the set of sections with small l, which called polished, where the probability of the correct term above threshold before step k is already sufficiently close to one that it is known in advance that it will not be necessary to continue to check these (as the subsequent false alarm probability would be quantified as larger than the small remaining improvement to correct detection probability for that section). Let polished_(k) (initially empty) be the set of terms in these sections. With the power decreasing, this coincides with a non-decreasing initial interval of sections.

Likewise there are the sections with large l where the probability of a correct detection on step k is less than the probability of false alarm, so it would be advantageous to still leave them untested. Let untested_(k) (desirably eventually empty) be the set of terms from these sections, corresponding to a decreasing tail interval of sections up to the last section L.

The complement is a middle region of terms

potential_(k) =J−polished_(k)−untested_(k),

corresponding to a window of sections, left_(k)≦l≦right_(k), worthy of attention in analyzing the performance at step k. For each term in this analysis window there is a reasonable chance (neither too high nor too low) of it being decoded by the completion of this step.

These middle regions overlap across k, so that for any term j has potential for being decoded in several steps.

In any particular realization of X, Y, some terms in this set potential_(k) are already in dec_(1,k−1). Accordingly, one has the option at step k to restrict the active set of the search to J_(k)=potential_(k)∪dec_(1,k−1) ^(c) rather than searching all of the set dec_(1,k−1) ^(c) not previously decoded. In this case one modifies the definitions of {circumflex over (q)}_(k) ^(tot) and {circumflex over (f)}_(k) ^(tot), to be

${\hat{q}}_{k}^{tot} = {\sum\limits_{j\mspace{14mu} {sent}}^{\;}{\pi_{j}1_{\{{\bigcup_{k^{\prime} \in K_{j,k}}\mathcal{H}_{k^{\prime},j}}\}}}}$ and ${\hat{f}}_{k}^{tot} = {\sum\limits_{j\mspace{14mu} {other}}^{\;}{\pi_{j}1_{\{{\bigcup_{k^{\prime} \in K_{j,k}}\mathcal{H}_{k^{\prime},j}}\}}}}$ where K_(j, k) = {k^(′) ≤ k:  j ∈ potential_(k^(′))}.

This refinement allows for analysis to show reduction in the total false alarms and corresponding improvement to the rate drop from capacity, when C is large.

4 Distributional Analysis

In this section the distributional properties of the random variables

_(k)=(

_(k,j): jεJ_(k)) for each k=1, 2, . . . , n are described. In particular it is shown for each k that

_(k,j) are location shifted normal random variables with variance near one for jεsent∪J_(k) and are independent standard normal random variables for jεother∪J_(k).

In Lemma 1 below the distributional properties of

₁ are derived. Lemma 2 characterizes the distribution of

_(k) for steps k≧2.

Before providing these lemmas a few quantities are defined which will be helpful in studying the location shifts of

hd k,j for jεsent∪J_(k). In particular, define the quantity

C _(j,R)=π_(j) Lν/(2R),

where π_(j)=P_(j)/P and ν=ν₁=P/(σ²+P). Likewise define

C _(j,R,B)=(C _(j,R))2 log B,

which also has the representation

C _(j,R,B) =nπjν.

The role of this quantity as developed below is via the location shift √{square root over (C_(j,R,B))} seen to be near √{square root over (C_(j,R))}τ. One compares this value to τ, that is, one compares C_(j,R) to 1 to see when there is a reasonable probability of some correct detections starting at step 1, and one arranges C_(j,R) to taper not too rapidly to allow decodings to accumulate on successive steps.

Recalling that π_(j)=π_((l))=P_((l))/P for j in section l, also denote the quantities defined above as

C _(l,R)=π_((l)) Lν(2R)

and c_(l,R,B)=C_(l,R)(2 log B) which is nπ_((l))ν.

Here are two illustrative cases. For the constant power allocation case, π_((l)) equals 1/L and C_(l,R) reduces to

C _(l,R) =R ₀ /R,

where R₀=(½)P/(σ²+P). This C_(l,R) is at least 1 when the rate R is not more than R₀.

For the case of power P_((l)) proportional to

, the value becomes π_((l))=

,

for l from 1 to L. Define

=(L/2)[1−],

which is essentially identical to

, for L large compared to C. Then

π_((l))=(2

/Lν)

and

C _(l,R)=(

/R)

.

For rates R not more than

, this C_(l,R) is at least 1 in some sections, leading to likelihood of some initial successes, and it tapers at the fastest rate at which decoding successes can still accumulate.

4.1 Distributional Analysis of the First Step:

The lemma for the distribution of

₁ is now given. Recall that J₁=J is the set of all N indices.

Lemma 1.

For each jεJ₁, the statistic

_(1,j) can be represented as

√{square root over (C _(j,R,B))}[χ_(n)/√{square root over (n)}]1_(j sent) +Z _(1,j),

where Z₁=(Z_(1,j): jεJ₁) is multivariate normal N(0,Σ₁) and χ_(n) ²=∥Y∥²/σ_(Y) ² is a Chi-square (n) random variable that is independent of Z₁. Here recall that σ_(Y) ²=P+σ² is the variance of each coordinate of Y.

The covariance matrix Σ₁ can be expressed as Σ₁=I−b₁b₁ ^(T), where b₁ is the vector with entries b_(1,j)=β_(j)/σ_(Y) for j in J.

The subscript 1 on the matrix Σ₁ and the vector b₁ are to distinguish these first step quantities from those that arise on subsequent steps.

Demonstration of Lemma 1:

Recall that the X_(j) for j in J are independent N(0, I) random vectors and that Y=Σ_(j)β_(j)X_(j)+ε, where the stun of squares of the β_(j) is equal to P.

Consider the decomposition of each random vector X_(j) of the dictionary into a vector in the direction of the received Y and a vector U_(j) uncorrelated with Y. That is, one considers the reverse regression

X _(j) =b _(1,j) Y/σ _(Y) +U _(j),

where the coefficient is b_(1,j)=

[X_(i,j)Y_(i)]/σ_(Y)=β_(j)/σ_(Y), which indeed makes each coordinate of U_(j) uncorrelated with each coordinate of Y. These coefficients collect into a vector b₁=β/σ_(Y) in

^(N).

These vectors U_(j)=X_(j)−b_(1,j)Y/σ_(Y) along with Y are linear combinations of joint normal random variables and so are also joint normal, with zero correlation implying that Y is independent of the collection of U_(j). The independence of Y and U_(j) facilitates development of distributional properties of the U_(j) ^(T)Y. For these purposes obtain the characteristics of the joint distribution of the U_(j) across terms j (clearly there is independence for distinct time indices i).

The coordinates of U_(j) and U_(j′) have mean zero and expected product 1_({j=j′})−b_(1,j)b_(1,j′). These covariances (

[U_(i,j)U_(j,j′)]: j,j′εJ) organize into a matrix

Σ₁ =Σ=I−Δ=I−bb ^(T).

For any constant vector α≠0, consider U_(j) ^(T)α/∥α∥. Its joint normal distribution across terms j is the same for any such α. Specifically, it is a normal N(0,Σ), with mean zero and the indicated covariances.

Likewise define the random variables Z_(j)=U_(j) ^(T)Y/∥Y∥, also denoted Z_(1,j) when making explicit that it is for the first step. Jointly across j, these Z_(j) have the normal N(0,Σ) distribution, independent of Y. Indeed, since the U_(j) are independent of Y, when conditioned on Y=α one gets the same N(0,ρ) distribution, and since this conditional distribution does not depend on Y, it is the unconditional distribution as well.

What this leads to is revealed via the representation of the inner product N_(j) ^(T)Y as b_(1,j)∥Y∥²/σ_(Y)+U_(j) ^(T)Y, which can be written as

${X_{j}^{T}Y} = {{\beta_{j}\frac{{Y}^{2}}{\sigma_{Y}^{2}}} + {{Y}{Z_{j}.}}}$

This identifies the distribution of the X_(j) ^(T)Y as that obtained as a mixture of the normal Z_(j) with scale and location shifts determined by an independent random variable χ_(n) ²=∥Y∥²/σ_(Y) ², distributed as Chi-square with n degrees of freedom.

Divide through by ∥Y∥ to normalize these inner products to a helpful scale and to simplify the distribution of the result to be only that of a location mixture of normals. The resulting random variables

_(1,j)=X_(j) ^(T)Y/∥Y∥ take the form

_(1,j) =√{square root over (n)}b _(1,j) |Y|/σ _(Y) +Z _(j),

where |Y|/σ_(Y)=χ_(n)/√{square root over (n)} is near 1. Note that √{square root over (n)}b_(1,j)=√{square root over (n)}β_(j)/σ_(Y) which is √{square root over (nπ_(j)ν)} or √{square root over (C_(j,R,B))}. This completes the demonstration of Lemma 1.

The above proof used the population reverse regression of X_(j) onto Y, in which the coefficient b_(1,j) arises as a ratio of expected products. There is also a role for the empirical projection decomposition, the first step of which is X_(j)=

_(1,j)Y/∥Y∥+V_(2,j), with G₁=Y. Its additional steps provide the basis for additional distributional analysis.

4.2 Distributional Analysis of Steps k≧2:

Let V_(k,j) be the part of X_(j) orthogonal to G₁, G₂, . . . , G_(k−1), from which G_(k) is obtained as Σ_(jεdec) _(k−1) √{square root over (P_(j))}V_(k,j). It yields the representation of the statistic

_(k,j)=X_(j) ^(T)G_(k)/∥G_(k)∥ as V_(k,j) ^(T)G_(k)/∥G_(k)∥, as said. Amongst other matters, the proof of the following lemma determines, for jεJ_(k), the ingredients of the regression V_(k,j)=b_(k,j)G_(k)/σ_(k)+U_(k,j) in which U_(k,j) is found to be a mean zero normal random vector independent of G_(k), conditioning on certain statistics from previous steps. Taking the inner product with the unit vector G_(k)/∥G_(k)∥ yields a representation of

_(k,j) as a mean zero normal random variable Z_(k,j) plus a location shift that is a multiple of ∥G_(k)∥ depending on whether j is in sent or not. The definition of Z_(k,j) is U_(k,j) ^(T)G_(k)/∥G_(k)∥.

Here the pattern used in Lemma 1 is maintained, using the calligraphic font

_(k,j) to denote the test statistics that incorporate the shift for j in sent and using the standard font Z_(k,j) to denote their counterpart mean zero normal random variables before the shift.

The lemma below characterizes the sequence of conditional distributions of the Z_(k)=(Z_(k,j): jεJ_(k)) and ∥G_(k)∥, given

_(k−1), for k=1, 2, . . . n, where

_(k−1)=(∥G _(k′) ∥,Z _(k′) : k′=1, . . . , k−1).

This determines also the distribution of

_(k)=(

_(k,j): jεJ_(k)) conditional on

_(k−1). Initializing with the distribution of

₁ derived in Lemma 1, the conditional distributions for all 2≦k≦n, are provided. The algorithm will be arranged to stop long before n, so these properties are needed only up to some much smaller final k=m. Note that J_(k) is never empty because at most L are decoded, so there must always be at least (B−1)L remaining. For an index set which may depend on the conditioning variables, let N_(J) _(k) (0,Σ) denote a mean zero multivariate normal distribution with index set J_(k) and the indicated covariance matrix.

Lemma 2.

For k≧2, given

_(k−1), the conditional distribution

_(Z) _(k,l) _(|)

_(k−1) of Z_(k,J) _(k) =(Z_(k,j): jεJ_(k)) is normal N_(J) _(k) (0,Σ_(k)); the random variable χ_(d) _(k) ²∥G_(k)∥²/σ_(k) ² is a Chi-square distributed, with d_(k)=n−k+1 degrees of freedom, conditionally independent of the Z_(k), where σ_(k) ² depends on

_(k−1) and is strictly positive provided there was at least one term above threshold on step k−1; and, moreover,

_(k,j) has the representation

−√{square root over (ŵ_(k) C _(j,R,B))}[χ_(d) _(k) /√{square root over (n)}]1_(j sent) +Z _(k,j).

The shift factor ŵ_(k) is the increment ŵ_(k)=ŝ_(k)−ŝ_(k−1), of the series ŝ_(k) with

${1 + {\hat{w}}_{2} + \ldots + {\hat{w}}_{k}} = {{\hat{s}}_{k} = \frac{1}{1 - {\left( {{\hat{q}}_{1}^{adj} + \ldots + {\hat{q}}_{k - 1}^{adj}} \right)v}}}$

where {circumflex over (q)}_(j) ^(adj)={circumflex over (q)}_(j)/(1+{circumflex over (f)}_(j)/{circumflex over (q)}_(j)), determined from weighted fractions of correct detections and false alarms on previous steps. Here ŝ₁=ŵ₁=1. The ŵ_(k) is strictly positive, that is, ŝ_(k) is increasing, as long as {circumflex over (q)}_(k−1)>0, that is, as long as the preceding step had at least one correct term above threshold. The covariance Σ_(k) has the representation

93 _(k) =I−δ _(k)δ_(k) ^(T) =I−ν _(k)β≈^(T) /P

where ν_(k)=ŝ_(k)ν, (Σ_(k))_(j,j′)=1_(j=j′)−δ_(k,j)δ_(k,j′), for j, j′, in J_(k), where the vector δ_(k) is in the direction β, with δ_(k,j)=√{square root over (ν_(k)P_(j)/P)}1_(j sent) for j in J_(k). Finally,

$\sigma_{k}^{2} = {\frac{{\hat{s}}_{k - 1}}{{\hat{s}}_{k}}{accept}_{k - 1}P}$

where accept_(k)=Σ_(jεdec) _(k) π_(j) is the size of the decoded set on step k.

The demonstration of this lemma is found in the appendix section 14.1. It follows the same pattern as the demonstration of Lemma 1 with some additional ingredients.

4.3 The Nearby Distribution:

Two joint probability measures

and

are now specified for all the Z_(k,j), jεJ and the ∥G_(k)∥ for k=1, . . . m. For

, it is to have the conditionals

specified above.

The

is the approximating distribution. Choose

to make all the Z_(k,j), for jεJ, for k=1, 2, . . . , m, be independent standard normal, and like

, choose

to make the χ_(n−k+1) ²=∥G_(k)∥²/σ_(k) ² be independent Chi-square(n−k+1) random variables.

Fill out of specification of the distribution assigned by

, via a sequence of conditionals

for Z_(k,J)=(Z_(k,j): jεJ), which is for all j in J, not just for j in J_(k). Here

_(k) ^(full)=(∥G_(k′)∥, Z_(k′,J): k′=1, 2, . . . , k). For the variables Z_(k,J) _(k) that actually used, the conditional distribution is that of

as specified in the above Lemma. Whereas for the Z_(k,j) with j in the already decoded set J−J_(k)=dec_(1,k−1), given

_(k−1), it is convenient to arrange them to have the same independent standard normal as is used by

. This completes the definition of the Z_(k,j) for all j, and with it one likewise extends the definition of

_(k,j) as a function of Z_(k,j) and ∥G_(k)∥ and completes the definition of the events

_(k,j) for all j, used in the analysis.

This choice of independent standard normal for the distribution of Z_(k,j) given

_(k−1) for j in dec_(1,k−1), is contrary to what would have arisen in the proof of 2 from the inner product of U_(k,j) with G_(k)/∥G_(k)∥ if there one were to have looked there at such j with

=1 for earlier k′<k. Nevertheless, as said, there is freedom of choice of the distribution of these variables not used by the decoder. The present choice is a simpler extension providing a conditional distribution of (Z_(k,j): jεJ) that shares the same marginalization to the true distribution of (Z_(k,j): jεJ_(k)) given

_(k−1).

An event A is said to be determined by

_(k) if its indicator is a function of

_(k). As

_(k)=(χ_(n−k′+1), Z_(k′+1), Z_(k′,J) _(k′) : k′≦k), with a random index set J_(k) given as a function of preceding

_(k−1), it might be regarded as a tricky matter. Alternatively a random variable may be said to be determined by

_(k) if it is measurable with respect to the collection of random variables (∥G_(k′∥, Z) _(k′,j)1_({jεdec) _(1,k′−1) _(c) _(}), jεJ, 1≦k′≦k). The multiplication by the indicator removes the effect on step k′ of any Z_(k′,j) decoded on earlier steps, that is, any j outside J_(k′). Operationally, no advanced measure-theoretic notions are required, as the sequences of conditional densities being worked have explicit Gaussian form.

In the following lemma appeal to a sense of closeness of the distribution

to

, such that events exponentially unlikely under

remain exponentially unlikely under the governing measure

.

Lemma 3.

For any event A determined by

_(k),

[A]≦

[A]e^(kc) ⁰ ,

where c₀=(½)log(1+P.σ²). The analogous statement holds more generally for the expectation of any non-negative function of

_(k).

See the appendix, subsection 14.2, for the proof. The fact that c₀ matches the capacity

might be interesting, but it is not consequential to the argument. What matters for us is simply that if

[A] is exponentially small in L or n, then so is

[A].

4.4 Logic in Bounding Detections and False Alarms:

Simple logic concerning unions plays an important simplifying role in the analysis here to lower bound detection rates and to upper bound false alarms. The idea is to avoid the distributional complication of stuns restricted to terms not previously above threshold.

Here assume that dec_(k)=thresh_(k) each step. Section 7.2 discusses an alternative approach where dec_(k) is taken to be a particular subset of thresh_(k), to demonstrate slightly better reliability bounds for given rates below capacity.

Recall that with {circumflex over (q)}_(k)=Σ_(j sent∩J) _(k) π_(j)

as the increment of weighted fraction of correct detections, the total weighted fraction of correct detections {circumflex over (q)}_(k) ^(tot)={circumflex over (q)}₁+ . . . +{circumflex over (q)}_(k) up to step k is the same as the weighted fraction of the union Σ_(j sent)π_(j)

Accordingly, it has the lower bound

${\hat{q}}_{k}^{tot} \geq {\sum\limits_{j\mspace{14mu} {sent}}^{\;}{\pi_{j}1_{\mathcal{H}_{k,j}}}}$

based solely on the step k half-spaces, where the sum on the right is over all j in sent, not just those in sent∩J_(k). That this simpler form will be an effective lower bound on {circumflex over (q)}_(k) ^(tot) will arise from the fact that the statistic tested in

_(k,j) is approximately a normal with a larger mean at step k than at steps k′<k, producing for all j in sent greater likelihood of occurrence of

_(k,j) than earlier

_(k′,j).

Concerning this lower bound Σ_(j sent)π_(j)

, in what follows it is convenient to set {circumflex over (q)}_(1,k) to be the corresponding sum Σ_(j sent)π_(j)1_(H) _(k,j) using a simpler purified form H_(k,j) in place of

_(k,j). Outside of an exception event studied herein, this H_(k,j) is a smaller set that

_(k,j) and so then {circumflex over (q)}_(k) ^(tot) is at least {circumflex over (q)}_(1,k).

Meanwhile, with {circumflex over (f)}_(k)=Σ_(jεother∩J) _(k) π_(j)

as the increment of weighted count of false alarms, as seen, the total weighted count of false alarms cot {circumflex over (f)}_(k) ^(tot)={circumflex over (f)}₁+ . . . +{circumflex over (f)}_(k) is the same as Σ_(j other)π_(j)

. It has the upper bound

${\hat{f}}_{k}^{tot} \leq {{\sum\limits_{j\mspace{14mu} {other}}^{\;}{\pi_{j}1_{\mathcal{H}_{1,j}}}} + \ldots + {\sum\limits_{j\mspace{14mu} {other}}^{\;}{\pi_{j}{1_{\mathcal{H}_{k,j}}.}}}}$

Denote the right side of this bound {circumflex over (f)}_(1,k).

These simple inequalities permit establishment of likely levels of correct detections and false alarm bounds to be accomplished by analyzing the simpler forms Σ_(j sent)π_(j)

and Σ_(j other)π_(j)

without the restriction to the random set J_(k), which would complicate the analysis.

Refinement Using Wedges:

Rather than using the last half-space

_(k,j) alone, one may obtain a lower bound on the indicator of the union

_(1,j)∪ . . . ∪

_(k,j) by noting that it contains

_(k−1,j)∪H

_(k,j) expressed as the disjoint union of the events

_(k,j) and

_(k−1,j)∩

_(k,j). The latter event may be interpreted as a wedge (an intersection of two half-spaces) in terms of the pair of random variables

_(k−1,j) ^(comb) and

_(k,j). Accordingly, there is the refined lower bound on {circumflex over (q)}_(k) ^(tot)=Σ_(j sent)π_(j)

, given by

${\hat{q}}_{k}^{tot} \geq {{\sum\limits_{j\mspace{14mu} {sent}}^{\;}{\pi_{j}1_{\mathcal{H}_{k,j}}}} + {\sum\limits_{j\mspace{14mu} {sent}}^{\;}{\pi_{j}{1_{\mathcal{H}_{{k - 1},j}\bigcap\mathcal{H}_{k,j}^{c}}.}}}}$

With this refinement a slightly improved bound on the likely fraction of correct detections can be computed from determination of lower bounds on the wedge probabilities. One could introduce additional terms from intersection of three or more half-spaces, but it is believed that these will have negligible effect.

Likewise, for the false alarms, the union

_(1,j)∪ . . . ∪

_(k,j) expressed as the disjoint union of

_(k,j),

_(k−1,j)∩

_(k,j) ^(c), . . . ,

_(1,j)∩

_(2,j) ^(c)∩ . . . ∩

_(k,j) ^(c), has the improved upper-bound for its indicator given by the sum

1_(ℋ_(k, j)) + 1_(ℋ_(k − 1, j)⋂ℋ_(k, j)^(c)) + … + 1_(ℋ_(1, j)⋂ℋ_(2, j)^(c))

given by just one half-space indicator and k−1 wedge indicators. Accordingly, the weighted total fraction of false alarms {circumflex over (f)}_(k) ^(tot) is upper-bounded by the π weighted sum of these indicators for j in other. This leads to improved bounds on the likely fraction of false alarms from determination of upper bounds on wedge probabilities.

Accounting with the Optional Analysis Window:

In the optional restriction to terms in the set pot_(k)=potential_(k) for each step, the {circumflex over (q)}_(k) take the same form but with J_(k)=pot_(k)∩dec_(1,k−1) ^(c) in place of J_(k)=J∩dec_(1,k−1) ^(c). Accordingly the total weighted count of correct detections {circumflex over (q)}_(k) ^(tot)={circumflex over (q)}₁+ . . . +{circumflex over (q)}_(k) takes the form

${{\hat{q}}_{k}^{tot} \geq {\sum\limits_{j\mspace{14mu} {sent}}^{\;}{\pi_{j}1_{\{\bigcup_{k^{\prime},\mathcal{H}_{k^{\prime},j}}\}}}}},$

where the union for term j is taken for steps in the set {k′≦k: jεpot_(k′)}. These unions are non-empty for the terms j in pot_(1,k)=pot₁∪ . . . ∪pot_(k). For terms in sent it will be arranged that for each j there is, as k′ increases, an increasing probability (of purified approximations) of the set

_(k′,j). Accordingly, for a lower bound on the indicator of the union using a single set use

where max_(k,j) is the largest of {k′>k: jεpot_(k′)}. Thus in place of Σ_(j sent)π_(j)

, for the lower bound on the total weighted fraction of correct detections this leads to

${\hat{q}}_{k}^{tot} \geq {\sum\limits_{j \in {{sent}\bigcap{pot}_{1,k}}}^{\;}{\pi_{j}{1_{\mathcal{H}_{\max_{k,j}{,j}}}.}}}$

Likewise an upper bound on the total weighted fraction of false alarms is

${\hat{f}}_{k}^{tot} \leq {{\sum\limits_{j \in {{other}\bigcap{pot}_{1}}}^{\;}{\pi_{j}1_{\mathcal{H}_{1,j}}}} + \ldots + {\sum\limits_{j \in {{other}\bigcap{pot}_{k}}}^{\;}{\pi_{j}{1_{\mathcal{H}_{k,j}}.}}}}$

Again the idea is to have these simpler forms with single half-space events, but now with each sum taken over a more targeted deterministic set, permitting a smaller total false alarm bound.

This document does not quantify specifics of the benefits of the wedges and of the narrowed analysis window (or a combination of both). This is a matter of avoiding complication. But the matter can be revisited to produce improved quantification of mistake bounds.

4.5 Adjusted Sums Replace Sums of Adjustments:

The manner in which the quantities {circumflex over (q)}₁, . . . , {circumflex over (q)}_(k) and {circumflex over (f)}₁, . . . {circumflex over (f)}_(k) arise in the distributional analysis of Lemma 2 is through the sum

{circumflex over (q)} _(k) ^(adj,tot) ={circumflex over (q)} ₁ ^(adj)+ . . . +{circumflex over (q)}_(k) ^(adj)

of the adjusted values {circumflex over (q)}_(k) ^(adj)={circumflex over (q)}_(k)/(1+{circumflex over (f)}_(k)/{circumflex over (q)}_(k)). Conveniently, by Lemma 4 below, {circumflex over (q)}_(k) ^(adj,tot)≧{circumflex over (q)}_(k) ^(tot,adj). That is, the total of adjusted increments is at least the adjusted total given by

${\hat{q}}_{k}^{{tot},{adj}} = \frac{{\hat{q}}_{k}^{tot}}{1 + {{\hat{f}}_{k}^{tot}/{\hat{q}}_{k}^{tot}}}$

which may also be written

${\hat{q}}_{k}^{tot} - {\hat{f}}_{k}^{tot} + {\frac{\left( {\hat{f}}_{k}^{tot} \right)^{2}}{{\hat{q}}_{k}^{tot} + {\hat{f}}_{k}^{tot}}.}$

In terms of the total weighted count of tests above threshold accept_(k) ^(tot)={circumflex over (q)}_(k) ^(tot)+{circumflex over (f)}_(k) ^(tot) it is

${\hat{q}}_{k}^{{tot},{adj}} = {{accept}_{k}^{tot} - {2{\hat{f}}_{k}^{tot}} + {\frac{\left( {\hat{f}}_{k}^{tot} \right)^{2}}{{accept}_{k}^{tot}}.}}$

Lemma 4.

Let f₁, . . . , f_(k) and g₁, . . . , g_(k) be non-negative numbers. Then

${\frac{g_{1}}{1 + {f_{1}/g_{1}}} + \ldots + \frac{g_{k}}{1 + {f_{k}/g_{k}}}} \geq {\frac{g_{1} + \ldots + g_{k}}{1 + {\left( {f_{1} + \ldots + f_{k}} \right)/\left( {g_{1} + \ldots + g_{k}} \right)}}.}$

Moreover, both of these quantities exceed

(g ₁ + . . . +g _(k))−(f ₁ + . . . +f _(k)).

Demonstration of Lemma 4:

Form p_(k′)=f_(k′)/[f₁+ . . . +f_(k)] and interpret as probabilities for a random variable K taking values k′ from 1 to k. Consider the convex function defined by ψ(x)=x/(1+1/x). After accounting for the normalization, the left side is

[ψ(g_(K)/f_(K))] and the right side is ψ[

(g_(K)/f_(K))]. So the first claim holds by Jensen's inequality. The second claim is because g/(1+f/g) equals g−f/(1+f/g) or equivalently g−f+f²/(g+f), which is at least g−f. This completes the demonstration of Lemma 4.

This lemma is used to assert that ŝ_(k)=1/(1−{circumflex over (q)}_(k−1) ^(adj,tot)ν) is at least 1/(1−{circumflex over (q)}_(k−1) ^(tot/adj)ν). For suitable weights of combination this ŝ_(k) corresponds to a total shift factor, as developed in the next section.

5 Separation Analysis

In this section the extent of separation is explored between the distributions of the test statistics

_(k,j) ^(comb) for j sent versus other j. In essence, for j sent, the distribution is a shifted normal. The assignment of the weights λ used in the definition of

_(k,j) ^(comb) is arranged so as to approximately maximize this shift.

5.1 The Shift of the Combined Statistic:

Concerning the weights λ_(1,k), λ_(2,k), . . . , λ_(k,k), for notational simplicity hide the dependence on k and denote them simply by λ₁, . . . , λ_(k), as elements of a vector λ. This λ is to be a member of the simplex S_(k)={λ:λ_(k′)≧0,Σ_(k′=1) ^(k)λ_(k′)=1} in which the coordinates are non-negative and stun to 1.

With weight vector λ the combined test statistic

λ_(k,j) ^(comb) takes the form

shift_(λ,k,j)1_({j sent}) +Z _(λ,k,j) ^(comb)

where

Z _(λ,k,j) ^(comb)=√{square root over (λ₁)}Z_(1,j)−√{square root over (λ₂)}Z_(2,j)− . . . −√{square root over (λ_(k))}Z_(k,j).

For convenience of analysis, it is defined not just for jεJ_(k), but indeed for all jεJ, using the normal distribution for the Z_(k′,j) discussed above. Here

shift_(λ,k,j)=shift_(λ,k)√{square root over (C _(j,R,B))}

where shift_(λ,k) is

√{square root over (λ₁χ_(n) ² /n)}+√{square root over (λ_(k) ŵ ₂χ_(n−1) ² /n)}+ . . . +√{square root over (λ_(k) ŵ _(k)χ_(n−k+1) ² /n)},

where χ_(n−k+1) ²=∥G_(k)∥²/σ_(k) ². This shift_(λ,k) would be largest with λ_(k′) proportional to ŵ_(k′)χ_(n−k′+1) ².

Outside of an exception set A_(h) developed further below, these χ_(n−k′+1) ²/n are at least 1−h, with small positive h. Then shift_(λ,k) is at least √{square root over (1−h)} times

√{square root over (λ₁ ŵ ₁)}+√{square root over (λ_(k) ŵ ₂)}+ . . . +√{square root over (λ_(k) ŵ _(k))}.

The test statistic

_(k,j) ^(comb) along with its constituent Z.k,j^(comb) arises by plugging in particular choices of {circumflex over (λ)}. Most choices of these weights that arise in our development will depend on the data and those exact normality of Z_(k,j) ^(comb) does not hold. This matter is addressed using tools of empirical processes, to show uniformity of closeness of relative frequencies based on Z_(λ,k,j) ^(comb) to the expectations based on the normal distribution. This uniformity can be exhibited over all λ in the simplex S_(k). For simplicity it is exhibited over a suitable subset of it.

5.2 Maximizing Separation:

Setting λ_(k′) equal to ŵ_(k′)/(1+ŵ₂+ . . . +ŵ_(k)) for k′≦k would be ideal, as it would maximize the resulting shift factor √{square root over (λ₁)}+√{square root over (ŵ₂)}√{square root over (λ₂)}+ . . . +√{square root over (ŵ_(k))}√{square root over (λ_(k))}, for λεS_(k), making it equal √{square root over (1+ŵ₂+ . . . +ŵ_(k))}=√{square root over (ŝ_(k))}, where ŝ_(k)=1/(1−q_(k) ^(adj,tot)ν) and ŵ_(k′)=ŝ_(k′)−ŝ_(k′−1).

Setting λ_(k′) proportional to ŵ_(k′) may be ideal, but it suffers from the fact that without advance knowledge of sent and other, the decoder does not have access to the separate values of {circumflex over (q)}_(k)=Σ_(jεsent∩J) _(k) π_(j)

and {circumflex over (f)}_(k)=Σ_(jεother∩J) _(k) π_(j)

needed for precise evaluation of ŵ_(k). A couple of means are devised to overcome this difficulty. The first is to take advantage of the fact that the decoder does have accept_(k)=={circumflex over (q)}_(l)+{circumflex over (f)}_(k)=Σ_(jεJ) _(k) π_(j)

, which is the weighted count of terms above threshold on step k. The second is to use computation of ∥G_(k)∥²/n which is σ_(k) ²χ_(n−k+1) ²/n as an estimate of σ_(k) ² with which a reasonable estimate of ŵ_(k) is obtained. A third method is to use residuals as discussed in the appendix, though its analysis is more involved.

5.3 Setting Weights {circumflex over (λ)} Based on Accept_(k):

The first method uses accept_(k), in place of {circumflex over (q)}_(k′) ^(adj) where it arises in the definition of ŵ_(k′) to produce a suitable choice of λ_(k′). Abbreviate accept_(k) as acc_(k), when needed to allow certain expressions to be suitably displayed. This accept_(k) upperbounds {circumflex over (q)}_(k) and is not much greater that {circumflex over (q)}_(k) when suitable control of the false alarms is achieved.

Recall ŵ_(k)=ŝ_(k)−ŝ_(k−1) for k>1 so finding the common denominator it takes the form

${{\hat{w}}_{k} = \frac{{\hat{q}}_{k - 1}^{adj}v}{\left( {1 - {{\hat{q}}_{k - 1}^{{adj},{tot}}v}} \right)\left( {1 - {{\hat{q}}_{k - 2}^{{adj},{tot}}v}} \right)}},$

with the convention that {circumflex over (q)}₀ ^(adj)=0. Let ŵ_(k) ^(acc) be obtained by replacing {circumflex over (q)}_(k−1) ^(adj) with its upper bound of acc_(k−1)=accept_(k−1) and likewise replacing {circumflex over (q)}_(k−2) ^(adj,tot) and {circumflex over (q)}_(k−1) ^(adj,tot) with their upper bounds acc_(k−2) ^(tot) and acc_(k−1) ^(tot), respectively, with acc₀ ^(tot)=0. Thus as an upper bound on ŵ_(k) set

${{\hat{w}}_{k}^{acc} = \frac{{acc}_{k - 1}v}{\left( {1 - {{acc}_{k - 2}^{tot}v}} \right)\left( {1 - {{acc}_{k - 1}^{tot}v}} \right)}},$

where for k=1 set ŵ_(k) ^(acc)=ŵ_(k)=1. For k>1 this ŵ_(k) ^(acc) is also

$\frac{1}{1 - {{acc}_{k - 1}^{tot}v}} - {\frac{1}{1 - {{acc}_{k - 2}^{tot}v}}.}$

Now each accept_(k′) exceeds {circumflex over (q)}_(k′) ^(adj) and is less than {circumflex over (q)}_(k′) ^(adj)+2{circumflex over (f)}_(k′).

Then set proportional to {circumflex over (λ)}_(k′) proportional to ŵ_(k) ^(acc). Thus

${\hat{\lambda}}_{1} = \frac{1}{1 + {\hat{w}}_{2}^{acc} + \ldots + {\hat{w}}_{k}^{acc}}$

and for k′ from 2 to k one has

${\hat{\lambda}}_{k^{\prime}} = {\frac{{\hat{w}}_{k^{\prime}}^{acc}}{1 + {\hat{w}}_{2}^{acc} + \ldots + {\hat{w}}_{k}^{acc}}.}$

The shift factor

√{square root over ({circumflex over (λ)}₁)}+√{square root over ({circumflex over (λ)}₂ ŵ ²)}+ . . . +√{square root over ({circumflex over (λ)}_(k) ŵ _(k))}

is then equal to the ratio

$\frac{1 + \sqrt{{\hat{w}}_{2}^{acc}{\hat{w}}_{2}} + \ldots + \sqrt{{\hat{w}}_{k}^{acc}{\hat{w}}_{k}}}{\sqrt{1 + {\hat{w}}_{2}^{acc} + \ldots + {\hat{w}}_{k}^{acc}}}.$

From ŵ_(k′) ^(acc)≧ŵ_(k′) the numerator is at least 1+ŵ₂+ . . . +ŵ_(l)=ŝ_(k), equaling 1/(1−({circumflex over (q)}₁ ^(adj)+ . . . +{circumflex over (q)}_(k−1) ^(adj))ν), which per Lemma 4 is at least 1/(1−{circumflex over (q)}_(k−1) ^(tot,adj)ν). As for the sum in the denominator, it equals 1/(1−acc_(k−1) ^(tot)ν). Consequently, the above shift factor using {circumflex over (λ)} is at least

$\frac{\sqrt{1 - {{acc}_{k - 1}^{tot}v}}}{1 - {{\hat{q}}_{k - 1}^{{tot},{adj}}v}}.$

Recognizing that acc_(k−1) ^(tot) and {circumflex over (q)}_(k−1) ^(tot) are similar when the false alarm effects are small, it is desirable to express this shift factor in the form

$\sqrt{\frac{1 - {\hat{h}}_{f,{k - 1}}}{1 - {{\hat{q}}_{k - 1}^{{tot},{adj}}v}}},$

where ĥ_(f,k) for each k is a small term depending on false alarms.

Some algebra confirms this is so with

${\hat{h}}_{f,k} = {{\hat{f}}_{k}^{tot}\frac{\left( {2 - {{\hat{f}}_{k}^{tot}/{acc}_{k}^{tot}}} \right)v}{1 - {{\hat{q}}_{k}^{{tot},{adj}}v}}}$

which is less than the value 2{circumflex over (f)}_(k) ^(tot)ν/(1−ν) equal to 2{circumflex over (f)}_(k) ^(tot)snr. Except in cases of large snr this approach is found to be quite suitable.

To facilitate a simple empirical process argument, replace each acc_(k) by its value ┌acc_(k){tilde over (L)}┐/{tilde over (L)} rounded up to a rational of denominator {tilde over (L)} for some integer {tilde over (L)} large compared to k. This restricts the acc_(k) to a set of values of cardinality {tilde over (L)} and correspondingly the set of values of acc₁, . . . , acc_(k−1) determining ŵ₂ ^(acc), . . . , ŵ_(k) ^(acc) and hence {circumflex over (λ)}₁, . . . , {circumflex over (λ)}_(k) is restricted to a set of cardinality ({tilde over (L)})^(k−1). The resulting acc_(k) ^(tot) is then increased by at most k/{tilde over (L)} compared to the original value. With this rounding, one can deduce that

ĥ _(f,h)≦2{circumflex over (f)} _(k) ^(tot) snr+k/{tilde over (L)}.

Next proceed with defining natural exception sets outside of which {circumflex over (q)}_(k′) ^(tot) is at least a deterministic value q_(1,k′) and {circumflex over (f)}_(k′) ^(tot) is not more than a deterministic value f_(1,k′) for each k′ from 1 to k. This leads to {circumflex over (q)}_(k) ^(tot,adj) being at least q_(1,k) ^(adj), where

q _(1,k) ^(adj) =q _(1,k)/(1+f _(1,k) /q _(1,k))

and ĥ_(f,k) is at most h_(f,k)=2f_(1,k)snr, and likewise for each k′≦k. This q_(1,k) ^(adj) is regarded as an adjustment to q_(1,k) due to false alarms.

When rounding the acc_(k) to be rational of denominator {tilde over (L)}, it is accounted for by setting

h _(k,k)=2f _(1,k) snr+k/{tilde over (L)}.

The result is that the shift factor given above is at least the deterministic value given by √{square root over (1−h_(f,k−1))}/√{square root over (1−q_(1,k−1) ^(adj)ν)} near 1/√{square root over (1−q_(1,k−1) ^(adj)ν)}. Accordingly shift_({circumflex over (λ)},k,j) exceeds the purified value

${\sqrt{\frac{1 - h^{\prime}}{1 - {q_{1,{k - 1}}^{adj}v}}}\sqrt{C_{j,R,B}}},$

where 1−h′=(1−h_(f))(1−h), with h′=h+h_(f)−hh_(f), where h_(f)=h_(f,m−1) serves as an upper bound to the h_(f,k−1) for all steps k≦m. 5.4 Setting Weights {circumflex over (λ)} Based on Estimation of σ_(k) ²:

The second method entails estimation of ŵ_(k) using an estimate of σ_(k) ². For its development make use of the multiplicative relationship from Lemma 2,

${{\hat{s}}_{k} = {{\hat{s}}_{k - 1}\frac{{ACC}_{k - 1}}{\sigma_{k}^{2}}}},$

where ACC_(k−1)=acc_(k−1)P=Σ_(jεdec) _(k−1)P_(j) is the un-normalized weight of terms above threshold on step k−1. Accordingly, from ŵ_(k)=ŝ_(k)−ŝ_(k+1) it follows that

${{\hat{w}}_{k} = {{\hat{s}}_{k - 1}\left( {\frac{{ACC}_{k - 1}}{\sigma_{k}^{2}} - 1} \right)}},$

where the positivity of ŵ_(k) corresponds to ACC_(k−1)≧σ_(k) ². Also

${\hat{s}}_{k} = {\prod\limits_{k^{\prime} = 1}^{k - 1}\; {\frac{{ACC}_{k^{\prime} - 1}}{\sigma_{k^{\prime}}^{2}}.}}$

Recognize that each 1/σ_(k′) ²=χ_(n−k′+1) ²/∥G_(k′∥) ². Again, outside an exception set, replace each χ_(n−k′+1) ² by its lower bound n(1−h), obtaining the lower bounding estimates

${{\hat{w}}_{k}^{low} = {{\hat{s}}_{k - 1}^{low}\left( {\frac{{ACC}_{k - 1}}{{\hat{\sigma}}_{k}^{2}} - 1} \right)}},{where}$ ${\hat{s}}_{k}^{low} = {\prod\limits_{k^{\prime} = 1}^{k - 1}\; \frac{{ACC}_{k^{\prime} - 1}}{{\hat{\sigma}}_{k^{\prime}}^{2}}}$ with ${\hat{\sigma}}_{k}^{2} = {\max {\left\{ {\frac{{G_{k}}^{2}}{n\left( {1 - h} \right)},{ACC}_{k - 1}} \right\}.}}$

Initializing with ŝ₁ ^(low)=ŵ₁ ^(low)=1 again have ŵ_(k) ^(low)=ŝ_(k) ^(low)−ŝ_(k−1) ^(low) and hence

s _(k) ^(low)=1+ŵ ₂ ^(low)+ . . . +ŵ_(k) ^(low).

Set the weights of combination to be {circumflex over (λ)}_(k′)=ŵ_(k′) ^(low)/ŝ_(k) ^(low) with which the shift factor is

$\frac{1 + \sqrt{{\hat{w}}_{2}^{low}{\hat{w}}_{2}} + \ldots + \sqrt{{\hat{w}}_{k}^{low}{\hat{w}}_{k}}}{\sqrt{{\hat{s}}_{k}^{low}}}.$

Using ŵ_(k′)≧ŵ_(k′) ^(low) this is at least

${\frac{1 + {\hat{w}}_{2}^{low} + \ldots + {\hat{w}}_{k}^{low}}{\sqrt{{\hat{s}}_{k}^{low}}} = \sqrt{{\hat{s}}_{k}^{low}}},$

which is √{square root over (ŝ_(k))} times the square root of

$\prod\limits_{k^{\prime} = 1}^{k - 1}{\left( \frac{\left( {1 - h} \right)n}{\chi_{n - k^{\prime} + 1}^{2}} \right).}$

When using this method of estimating ŵ_(k) augment the exception set so that outside it one has χ_(n−k′+1) ²/n≦(1+h). Then the above product is at least [(1−h)/(1+h)]^(k−1) and the shift factor shift_({circumflex over (λ)},k) is at least

${\sqrt{{\hat{s}}_{k}\left( {1 - h^{\prime}} \right)} \geq \sqrt{\frac{1 - h^{\prime}}{1 - {q_{1,{k - 1}}^{adj}v}}}},$

where now 1−h′=(1−h)/(1+h)^(k−1). Here the additional (1−h) factor, as before, is to account in the definition of shift_({circumflex over (λ)},k) for lower bounding the χ_(n−k′+1) ²/n by (1−h).

Whether now the [(1−h)/(1−h)]^(k−1) is less of a drop than the (1−h_(f))=(1−2f_(k−1)snr) from before depends on the choice of h, the bound on the false alarms, the number of steps k and the signal to noise ratio snr.

Additional motivation for this choice of {circumflex over (λ)}_(k) comes from consideration of the tests statistics Z_(k,j) ^(res)=X_(j) ^(T)res_(k)/∥res_(k)∥ formed by taking the inner products of X_(j) with the standardized residuals, where res_(k) denotes the difference between Y and its projection onto the span of F₁, F₂, . . . , F_(k−1). It is shown in the appendix that these statistics have the same representation but with λ_(k′)=w_(k′)/s_(k), for k′≦k, where s_(k)=∥Y∥²/∥res_(k)∥² and w_(k)=s_(k)−s_(k−1), again initialized with s₁=w₁=1. In place of the iterative rule developed above

${{\hat{s}}_{k} = {{{\hat{s}}_{k - 1}\frac{{ACC}_{k - 1}}{\sigma_{k}^{2}}} = {{\hat{s}}_{k - 1}\frac{{ACC}_{k - 1}\chi_{n - k + 1}^{2}}{{G_{k}}^{2}}}}},$

these residual-based s_(k) are shown there to satisfy

$s_{k} = {s_{k - 1}\frac{{{\overset{\sim}{F}}_{k - 1}}^{2}}{{G_{k}}^{2}}}$

where {tilde over (F)}_(k−1) is the part of F_(k−1) orthogonal to the previous F_(k′) for k′=1, . . . , k−2.

Intuitively, given that the coordinates of X_(j) are i.i.d. with mean 0 and variance 1, this ∥{tilde over (F)}_(k−1)∥² should not be too different from ∥F_(k−1)∥² which should not be too different from n AXX_(k−1). So these properties give additional motivation for this choice. It is also tempting to try to see whether this λ based on the residuals could be amenable to the method of analysis, herein. It would seem that one would need additional properties of the design matrix X, such as uniform isometry properties of subsets of certain sizes. However, it is presently unclear whether such properties could be assured without harming the freedom to have rate up to capacity. For now stick to the simpler analysis based on the estimates here of the ŵ_(k) that maximizes separation.

5.5 Exception Events and Purified Statistics:

Consider more explicitly the exception events

A _(q)=∪_(k′=1) ^(k−1) {{circumflex over (q)} _(k′) ^(tot) <q _(1,k′)}

and

A _(f)=∪_(k′=1) ^(k−1) {{circumflex over (f)} _(k′) ^(tot) >f _(1,k′)}.

As said, one may also work with the related events ∪_(k′=1) ^(k−1){{circumflex over (q)}_(1,k′)<q_(1,k′)} and ∪_(k′=1) ^(k−1){{circumflex over (f)}_(1,k′)>f_(1,k′)}.

Define the Chi-square exception event A_(h) to include

∪_(k′=1) ^(k){χ_(n−k′+1) ² /n≦1−h}

or equivalently ∪_(k′=1) ^(k){χ_(n−k′+1) ²/(n−k′+1)≦(1−h_(k′))} where h_(k′) is related to h by the equation (n−k′+1)(1−h_(k′))=n(1−h). For the second method it is augmented by including also

∪_(k′=1) ^(k){χ_(n−k′+1) ² /n≧1+h}.

The overall exception event is A=A_(q)∪A_(f)∪A_(h). When outside this exception set, the shift_({circumflex over (λ)},k,j) exceeds the purified value given by

${shift}_{k,j} = {\sqrt{\frac{1 - h^{\prime}}{1 - {q_{1,{k - 1}}^{adj}v}}}{\sqrt{C_{j,R,B}}.}}$

Recalling that C_(j,R,B)=π_(j)νL(log B)/R the factor 1−h′ may be absorbed into the expression by letting

C _(j,R,B,h) =C _(j,R,B)(1−h′).

Or in terms of the section index l write C_(l,R,B,h)=C_(l,R,B)(1−h′). Then the above lower bound on the shift may be expressed as

$\sqrt{\frac{C_{j,R,B,h}}{1 - {x\; v}}}$

evaluated at x=q_(1,k−1) ^(adj), also denoted as

${shift}_{\;,x} = \sqrt{\frac{C_{,R,B,h}}{1 - {x\; v}}}$

For λ in S_(k), set H_(λ,k,j) to be the purified event that the approximate combined statistic shift_(k,j)1_(j sent)+Z_(λ,k,j) ^(comb) is at least the threshold τ. That is,

H _(λ,k,j)={shift_(k,j)1_(j sent) +Z _(λ,k,j) ^(comb)≧τ},

where in contrast to

_(k,j)={

k,j^(comb)≧τ} a standard rather than a calligraphic font is used for this event H_(λ,k,j) based on the normal Z_(λ,k,j) ^(comb) with the purified shift.

Recall that the coordinates of λ, denoted λ_(k′,k) for k′=1, 2, . . . k, have dependence on k. For each k, the λ_(k′,k) can be determined from normalization of segments of the first k in sequences w₁, w₂, . . . , w_(m) of positive values. With an abuse of notation, also denote the sequence for k=1, 2, . . . , m of such standardized combinations Z_(λ,k,j) ^(comb) as

$Z_{w,k,j}^{comb} = {\frac{{\sqrt{w_{1}}Z_{1,j}} - {\sqrt{w_{2}}Z_{2,j}} - \ldots - {\sqrt{w_{k}}Z_{k,j}}}{\sqrt{w_{1} + w_{2} + \ldots + w_{k}}}.}$

In this case the corresponding event H_(λ,k,j) is also denoted H_(w,k,j).

Exept in A_(q)∪A_(f)∪A_(h), the event

_(k,j) contains H_({circumflex over (λ)},k,j) also denote as H_(ŵ) _(acc) _(,k,j) or H_(ŵ) _(low) _(,k,j), respectively, for the two methods of estimating ŵ.

Also, as for the actual test statistics, the purified forms satisfy the updates

Z _(λ,k,j) ^(comb)=√{square root over (1−λ_(k))}Z_(λ,k−1) ^(comb)−√{square root over (λ_(k))}Z_(k,j)

where λ_(k)=λ_(k,k).

5.6 Definition of the Update Function:

Via C_(j,R,B) the expression for the shift is decreasing in R. Smaller R produce a bigger shift and greater statistical distinguishability between the terms sent and those not sent. This is a property commensurate with the communication interest in the largest R for which after a suitable number of steps one can reliable distinguish most of the terms.

Take note for j sent that shift_(k,j) is equal to

${\mu_{j}(x)} = \sqrt{\frac{_{j,R,B,h}}{1 - {xv}}}$

evaluated at x=q_(l,k−1) ^(adj). To bound the probability with which a term sent is successfully detected by step k, examine the behavior of

Φ(μ_(j)(x)−τ)

which, at that x, is the

probability of the purified event H_(λ,k,j) for in sent, based on the standard normal cumulative distribution of Z_(λ,k,j) ^(comb). This Φ(μ_(j)(x)−τ) is increasing in x.

For constant power allocation the contributions Φ(μ_(j)(x)−τ) are the same for all j in sent, whereas, for decreasing power assignments, one has a variable detection probability. Note that it is greater than ½ for those j for which μ_(j)(x) exceeds τ. As x increases, there is a growing set of sections for which μ_(j)(x) sufficiently exceeds τ, such that these sections have high

probability of detection.

The update function g_(L)(x) is defined as the π weighted average of these Φ(μ_(j)(x)−τ) for j sent, namely,

${g_{L}(x)} = {\sum\limits_{j\mspace{14mu} {sent}}\; {\pi_{j}{\Phi \left( {{\mu_{j}(x)} - \tau} \right)}}}$

or, equivalently,

${{g_{L}(x)} = {\sum\limits_{ = 1}^{L}\; {\pi_{()}{\Phi \left( {{shift}_{,x} - \tau} \right)}}}},$

an L term sum. That is, g_(L)(x) is the

expectation of the sample weighted fraction Σ_(j sent)π_(j)1_(H) _(λ,k,j) for any A in S_(k). The idea is that for any given x this sample weighted fraction will be near g_(L)(x), except in an event of exponentially small probability.

This update function g _(L) on g_(L) on [0,1] indeed depends on the power allocation π as well as the design parameters L, B, R, and the value a determining τ=√{square root over (2 log B)}+a. Plus it depends on the signal to noise ratio via ν=snr/(1+snr). The explicit use of the subscript L is to distinguish the stun g_(L)(x) from an integral approximation to it denoted g that will arise later below.

6 Detection Build-up with False Alarm Control

In this section, target false alarm rates are set and a framework is provided for the demonstration of accumulation of correct detections in a moderate number of steps.

6.1 Target False Alarm Rates:

A target weighted false alarm rate for step k arises as a bound f* on the expected value of Σ_(j other)π_(j)1_(H) _(q,j,k) . This expected value is (B−1) Φ(τ), where Φ(τ) is the upper tail probability with which a standard normal exceeds the threshold τ=√{square root over (2 log B)}+a. A tight bound is

$\frac{1}{\left( {\sqrt{2\mspace{11mu} \log \mspace{11mu} B} + a} \right)\sqrt{2\pi}}\exp {\left\{ {{{- a}\sqrt{2\mspace{11mu} \log \mspace{11mu} B}} - {\left( {1/2} \right)a^{2}}} \right\}.}$

There is occasion to make use of the similar choice of f* equal to

$\frac{1}{\left( \sqrt{2\mspace{11mu} \log \mspace{11mu} B} \right)\sqrt{2\pi}}\exp {\left\{ {{- a}\sqrt{2\mspace{11mu} \log \mspace{11mu} B}} \right\}.}$

The fact that these indeed upper bound B Φ(τ) follows from Φ(x)≦φ(x)/x for positive x, with φ being the standard normal density. Likewise set f>f*. Express f=ρf* with ρ>1, w Across the steps k, the choice of constant a_(k)=a produces constant f_(k)*=f* with stun f_(1,k)* equal to kf*. Furthermore, set f_(1,k)>f_(1,k), which arises in upper bounding the total false alarm rate. In particular, it is arranged for the ratio f_(1,k)/f_(1,k)* to be at least as large as a fixed ρ>1.

At the final step m, let

f*=f _(1,m) *=mf*

be the baseline total false alarm rate, and use f=f_(1,m), typically equal to ρ f*, to be a value which will be shown to likely upper bound Σ_(j other)π_(j)1_(∪) _(k=1) _(m) _(H) _(q,j,k) .

As will be explored soon, it is needed for f_(1,k) to stay less than a target increase in the correct detection rate each step. As this increase will be a constant times 1/log B, for certain rates close to capacity, this will then mean that f and hence f* need to be bounded by a multiple of 1/log B. Moreover, the number of steps m will be of order log B. So with f*=mf* this means f* is to be of order 1/(log B)². From the above expression for f*, this will entail choosing a value of a near

( 3/2)(log log B)/√{square root over (2 log B)}.

6.2 Target Total Detection Rate:

A target total detection rate q_(1,k)* and the associated values q_(1,k) and q_(1,k) ^(adj) are recursively defined using the function g_(L)(x).

In particular, per the preceding section, let

$q_{1,k}^{*} = {\sum\limits_{j\mspace{14mu} {sent}}\; {\pi_{j}{\Phi \left( {{shift}_{k,j} - \tau} \right)}}}$

which is seen to be

q _(1,k) *=g _(L)(x)

evaluated at x=q_(1,k−1) ^(adj). The convention is adopted at k=1 that the previous q_(k−1) and x=q_(1,k−1) ^(adj) are initialized at 0. To complete the specification, a sequences of small positive η_(k) are chosen with which it is set that

_(1,k) =q _(1,k)*−η_(k).

For instance one may set η_(k)=η. The idea is that these η_(k) will control the exponents of tail probabilities of the exception set outside of which {circumflex over (q)}_(k) ^(tot) exceeds q_(1,k). With this choice of q_(1,k) and f_(1,k) one has also

q _(1,k) ^(adj) =q _(1,k)/(1+f _(1,k) /q _(1,k)).

Positivity of the gap g_(L)(x)−x provides that q_(1,k) is larger than q_(1,k−1) ^(adj). As developed in the next subsection, the contributions from η_(k) and f_(1,k) are arranged to be sufficiently small that q_(1,k) ^(adj) and q_(1,k) are increasing with each such step. In this way the analysis will quantify as x increases, the increasing proportion that are likely to be above threshold.

6.3 Building Up the Total Detection Rate:

Let's give the framework here for how the likely total correct detection rate q_(1,k) builds up to a value near 1, followed by the corresponding conclusion of reliability of the adaptive successive decoder. Here the notion of correct detection being accumulative is defined. This notion holds for the power allocations studied herein.

Recall that with the function g_(L)(x) defined above, for each step, one updates the new q_(1,k) by choosing it to be slightly less than q_(1,k)*=q_(L)(q_(1,k−1) ^(adj)). The choice of q_(1,k), is accomplished by setting a small positive η_(k) for which q_(1,k)=q_(1,k)*−η_(k). These may be constant, that is η_(k)=η, across the steps k=1, 2, . . . , m.

There are slightly better alternative choices for the η_(k) motivated by the reliability bounds. One is to arrange for D(q_(1,k)∥q_(1,k)*) to be constant where D is the relative entropy between Bernoulli random variables of the indicated success probabilities. Another is to arrange η_(k) such that η_(k)/√{square root over (V_(k))} is constant, where V_(k)=V(x) evaluated at x=q_(1,k−1) ^(adj), where

${{V(x)}/L} = {\sum\limits_{j\mspace{14mu} {sent}}\; {\pi_{j}{\Phi \left( {\mu_{j}(x)} \right)}{{\overset{\_}{\Phi}\left( {\mu_{j}(x)} \right)}.}}}$

This V_(k)/L may be interpreted as a variance of {circumflex over (q)}_(1,k) as developed below. The associated standard deviation factor √{square root over (V(x))} is shown in the appendix to be proportional to (1−xν).

With evaluation at x=q_(1,k−1) ^(adj), this gives rise to η_(k)=η(x) equal to (1−xν) times a small constant.

How large one can pick η_(k) will be dictated by the size of the gap g_(L)(x)−x at x=q_(1,k−1) ^(adj).

Let x* be any given value between 0 and 1, preferably not far from 1.

Definition:

A positive increasing function g(x) bounded by 1 is said to be accumulative for 0≦x≦x* if there is a function gap(x)>0, with

g(x)−x≧gap(x)

for all 0≦x≦x*. An adaptive successive decoder with rate and power allocation chosen so that the update function g_(L)(x) satisfies this property is likewise said to be accumulative. The shortfall is defined by δ*=1−g_(L)(x*).

If the update function is accumulative and has a small shortfall, then it is demonstrated, for a range of choices of η_(k)>0 and f_(1,k)>k_(1,k)*, that the target total detection rate q_(1,k) increases to a value near 1 and that the weighted fraction mistakes is with high probability less than δ_(k)=(1−q_(1,k))+f_(1,k). This mistake rate δ_(k) is less than 1−x* after a number of steps, and then with one more step it is further reduced to a value not much more than δ*=1−g_(L)(x*), to take advantage of the amount by which g_(L)(x*) exceeds x*.

The tactic in providing good probability exponents will be to demonstrate, for the sparse superposition code, that there is an appropriate size gap. It will be quantified via bounds on the minimum of the gap or the minimum of the ratio gap(x)/(1−xν) that arises in a standardization of the gap, where the minimum is taken for 0≦x≦x*.

The following lemmas relate the sizes of η and f and the number of steps m to the size of the gap.

Lemma 5.

Suppose the update function g_(L)(x) is accumulative on [0,xx*] with g_(L)(x)−x≧gap for a positive constant gap>0. Arrange positive constants η and f and m*≧2, such that

η+ f+1/(m*−1)=gap.

Suppose f_(1,k)≦ f as arises from f_(1,k)= f or from f_(1,k)=kf for each k≦m* with f= f/m*. Set q_(1,k)=q_(1,k)*−η. Then q_(1,k) is increasing on each step for which q_(1,k−1)−f_(1,k−1)≦x*, and, for such k the increment q_(1,k)−q_(1,k−1) is at least 1/(m*−1). The number of steps k=m−1 required such that q_(1,k)−f_(1,k) first exceeds x*, is bounded by m*−1. At the final step m≦m*, the weighted fraction of mistakes target δ_(m)=(1−q_(1,m))+f_(1,m) satisfies

δ_(m) ≦δ*+η+ f.

The value δ_(m)=(1−q_(1,m))+f_(1,m) is used in controlling the sum of weighted fractions of failed detections and of false alarms.

In the decomposition of the gap, think of η and f as providing portions of the gap which contribute to the probability exponent and false alarm rate, respectively, whereas the remaining portion controls the number of steps.

The following is an analogous conclusion for the case of a variable size gap bound. It allows for somewhat greater freedom in the choices of the parameters, with η_(k) and f_(1,k) determined by functions η(x) and f(x), respectively, evaluated at x=q_(1,k−1) ^(adj).

Lemma 6.

Suppose the update function is accumulative on [0,x*]. Choose positive functions η(x) and f(x) on [0,x*] with gap(x)−η(x)− f(x) not less than a positive value denoted gap′. Suppose q_(1,k)=q_(1,k)*−η_(k) where η_(k)≦η(q_(1,k−1) ^(adj)) and f_(1,k)≦ f(q_(1,k−1) ^(adj)). Then q_(1,k)−q_(1,k−1)>gap′ on each step for which q_(1,k−1) ^(adj)≦x* and the number of steps k such that the q_(1,k) ^(adj) first exceeds x* is bounded by 1/gap′. With a number of steps m≦1+1/gap′, the δ_(m)=(1−q_(1,m))+f_(1,m) satisfies

δ_(m)≦δ*+η_(m) +f _(1,m).

The proofs for Lemmas 5 and 6 are given in Appendix 14.3. One has the choice whether to be bounding the number of steps such that q_(1,k) ^(adj) first exceeds x* or such that the slightly smaller value q_(1,k)−f_(1,k) first exceeds x*. The latter provides the slightly stronger conclusion that δ_(k)≦1−x*. Either way, at the penultimate step q_(1,k) ^(adj) is at least x*, which is sufficient for the next step m=k+1 to take us to a larger value of q_(1,m)* at least g_(L)(x*). So either formulation yields the stated conclusion.

Associated with the use of the factor (1−xν) there is the following improved conclusion, noting that GAP is necessarily larger than the minimum of gap(x).

Lemma 7.

Suppose that g_(L)(x)−x is at least gap(x)=(1−xν)GAP for 0≦x≦x* with a positive GAP. Again there is convergence of g_(1,k) to values at least x*. Arrange positive η_(std) and m* with

${GAP} = {\eta_{std} + {\frac{\log \mspace{11mu} {1/\left( {1 - x^{*}} \right)}}{m^{*} - 1}.}}$

Set η(x)=(1−xν)η_(std) and f≦(1−ν)GAP′ with GAP′=[log 1/(1−x*)]/(m*−1) and set η_(k)=η(x) at x=q_(1,k−1) ^(adj) and f_(1,k)≦ f. Then the number of steps k=m−1 until zx_(k) first exceeds x* is not more than m*−1. Again at step m the δ_(m)=(1−q_(1,m))+f_(1,m) satisfies δ_(m)≦δ*+η_(m)+ f.

Demonstration of Lemma 7:

One has

q _(1,k) =g _(L)(q _(1,k−1) ^(adj))−η(q _(1,k−1) ^(adj))

at least

q _(1,k−1) ^(adj)+(1−q _(1,k−1) ^(adj)ν)(GAP−η _(std)).

Subtracting f as a bound on f_(1,k), it yields

q _(1,k) ^(adj) ≧q _(1,k−1) ^(adj)+(1−q _(1,k−1) ^(adj))νGAP′.

This implies, with x_(k)=g_(1,k) ^(adj) and ε=νGAP′, that

x _(k)≧(1−ε)X _(k−1)+ε

or equivalently,

(1−x _(k))≦(1−ε)(1−x _(k−1)),

as long as X_(k−1)≦x*. Accordingly for such k, there is the exponential bound

(1−x _(k))≦(1−ε)^(k) ≦e ^(−εk) =e ^(−νGAP′k)

and the number of steps k=m−1 until x_(k) first exceeds x* satisfies

${m - 1} \leq \frac{\log \mspace{11mu} {1/\left( {1 - x^{*}} \right)}}{\log \mspace{11mu} {1/\left( {1 - \varepsilon} \right)}} \leq {\frac{\log \mspace{11mu} {1/\left( {1 - x^{*}} \right)}}{v\mspace{11mu} {GAP}^{\prime}}.}$

This bound is mA*−1. The final step takes q_(1,m)* to a value at least g_(L)(x*) so δ_(m)≦δ*+η_(m)+f_(1,m). This completes the demonstration of Lemma 7.

The idea here is that by extracting the factor (1−xν), which is small if x and ν are near 1, it follows that a value GAP with larger constituents η_(std) and GAP′ can be extracted than the previous constant gap, though to do so one pays the price of the log 1/(1-x*) factor.

Concerning the choice of f_(1,k), consider setting f_(1,k)= f for all k from 1 to m. This constant k_(1,k)= f remains bigger than f_(1,k)*=kf* with minimum ratio f/ f* at least ρ>1. To give a reason for choosing a constant false alarm bound, note that with f_(1,k) equal to f_(1,m)= f, it is greater than f_(1,m)*= f*, which exceeds f_(l,k)* for k<m. Accordingly, the relative entropy exponent (B−1)D(p_(1,k)∥p_(1,k)*) that arises in the probability bound in the next section is smallest at k=m, where it is at least f

(ρ)/ρ, where

(ρ) is the positive value ρ log ρ−(ρ−1).

In contrast, one has the seemingly natural choice f_(1,k)=kf of linear growth in the false alarm bound, with f=f*ρ. It is also upper bounded by f for k≦m and has constant ratio f_(1,k)/f_(1,k)* equal to ρ. It yields a corresponding exponent of kf

(ρ)/ρ for k=1 to Trl. However, this exponent has a value at k=1 that can be seen to be smaller by a factor of order 1/m. For the same final false alarm control, it is preferable to arrange the larger order exponent, by keeping D(p_(1,k)∥p_(1,k)*) at least its value at k=m.

7 Reliability of Adaptive Successive Decoding

Herein it is established, for any power allocation and rate for which the decoder is accumulative, the reliability with which the weighted fractions of mistakes are governed by the studied quantities 1−q_(1,m) plus f_(1,m). The bounds on the probabilities with which the fractions of mistakes are worse than such targets are exponentially small in L. The implication is that if the power assignment and the communication rate are such that the function q_(L) is accumulative on [0,x*], then for a suitable number of steps, the tail probability for weighted fraction of mistakes more than δ*=1−q_(L)(x*) is exponentially small in L.

7.1 Reliability Using the Data-Driven Weights:

In this subsection reliability is demonstrated using the data-driven weights {circumflex over (λ)} in forming the statistic

_(k,j) ^(comb). Subsection 7.2 discusses a slightly different approach which uses deterministic weights and provides slightly smaller error probability bounds.

Theorem 8.

Reliable communication by sparse superposition codes with adaptive successive decoding. With total false alarm rate targets f_(1,k)>f_(1,k)* and update function g_(L), set recursively the detection rate targets q_(1,k)=g_(L)(q_(1,k−1) ^(adj))−η_(k), with η_(k)=q_(1,k)*−q_(1,k)>0 set such that it yields an increasing sequence q_(1,k) for steps 1≦k≦m. Consider {circumflex over (δ)}_(m), the weighted failed detection rate plus false alarm rate. Then the m step adaptive successive decoder incurs {circumflex over (δ)}_(m) less than δ_(m)=(1−q_(1,m))+f_(1,m), except in an event of probability with upper bound as follows:

$\begin{matrix} {{{\sum\limits_{k = 1}^{m}\; \left\lbrack ^{{{- L_{\pi}}{D{({{q_{1,k}{q_{1,k}^{*})}} + {{({k - 1})}\log \mspace{11mu} \overset{\sim}{L}}})}}} + {c_{0}k}} \right\rbrack} + \; {\sum\limits_{k = 1}^{m}\; \left\lbrack ^{{- {L_{\pi}{({B - 1})}}}{D({{p_{1,k}{p_{1,k}^{*})}} + {{({k - 1})}\log \mspace{11mu} \overset{\sim}{L}}}}} \right\rbrack} + {\sum\limits_{k = 1}^{m}\; ^{{- {({n - k + 1})}}D_{h_{k}}}}},} & \left. I \right) \end{matrix}$

-   -   where the terms correspond to tail probabilities concerning,         respectively, the fractions of correct detections, the fractions         of false alarms, and the tail probabilities for the events         {∥G∥_(k) ²/σ_(k) ²≦n(1−h)}, on steps 1 to m. Here         L_(π)=1/max_(j) π_(j). The p_(1,k)p_(1,k)* equal the         corresponding f_(1,k), f_(1,k)* divided by B−1. Also         D_(h)=−log(1−h)−h is at least, h²/2. Here         h_(k)=(nh−k+1)/(m−k+1), so the exponent (n−k+1)D_(h) _(k) is         near nD_(h), as long as k/n is small compared to h.         II) A Refined Probability Bound Holds as in I Above but with         Exponent

$L\frac{\eta_{k}^{2}}{V_{k} + {\left( {1/3} \right){\eta_{k}\left( {L/L_{\pi}} \right)}}}$

-   -   in place of L_(π)D(q_(1,k)∥q_(1,k)*) for each k=1, 2, . . . , m.

Corollary 9.

Suppose the rate and power assignments of the adaptive successive code are such that g_(L) is accumulative on [0,x*] with a positive constant gap and a small shortfall δ*=1−g_(L)(x*). Assign positive η_(k)=η and f_(1,k)= f and m≧2 with 1−q_(1,m)≦δ*+η. Let

(ρ)=ρ log ρ−(ρ−1). Then there is a simplified probability bound. With a number of steps in, the weighted failed detection rate plus false alarm rate is less than δ*+η+ f, except in an event of probability not more than.

me ^(−2L) ^(η) ² ^(+m[c) ⁰ ^(+log {circumflex over (L)}]) +me ^(−L) ^(π)

^(/ρ+m log {tilde over (L)})+me^(−(n−m+1)h) ^(m) ² ^(/2).

The bound in the corollary is exponentially small in 2L_(π)η² if h is chosen such that (n−m+1)h_(m) ²/2 is at least 2L_(π)η² and ρ>1 and f are chosen such that f[log ρ−1+1/ρ] matches 2η².

Improvement is possible using II, in which case it is found that V_(k) is of order 1/√{square root over (log B)}. This produces a probability bound exponentially small in Lη²(log B)^(1/2) for small η.

Demonstration of Theorem 8 and its Corollary:

False alarms occur on step k, when there are terms j in other ∪J_(k) for which there is occurrence of the event

_(k,j), which is the same for such j in other as the event H_(w) _(acc) _(,k,j), as there is no shift of the statistics for j in other. The weighted fraction of false alarms up to step k is {circumflex over (f)}₁+ . . . +{circumflex over (f)}_(k) with increments {circumflex over (f)}_(k)=Σ_(jεother∪J) _(k) π_(j)

. This increment excludes the terms in dec_(1,k−1) which are previously decoded. Nevertheless, introducing associated random variables for these excluded events (with the distribution discussed in the proof of Lemmas 1 and 2), the sum may be regarded as the weighted fraction of the union Σ_(jεother)π_(j)

.

Recall, as previously discussed, for all such j in other, the event H_(w,k′,j) is the event that Z_(w,k′,j) ^(comb) exceeds τ, where for each w=(1, w₂, w₃, . . . , w_(k)), the Z_(w,k′,j) ^(comb) are standard normal random variables, independent across j in other. So the events ∪_(k′=1) ^(k)H_(w,k′,j) are independent and equiprobable across such j. Let p_(1,k)* be their probability or an upper bound on it, and let p_(1,k)>p_(1,k)*. Then A_(f,k)={{circumflex over (f)}_(k) ^(tot)≧f_(1,k)} is contained in the union over all possible w of the events {{circumflex over (p)}_(w,1,k)≧p_(1,k)} where

${\hat{p}}_{w,1,k} = {\frac{1}{B - 1}{\sum\limits_{j \in {other}}\; {\pi_{j}{1_{\bigcup_{k^{\prime} = 1}^{k}H_{w,k^{\prime},j}}.}}}}$

With the rounding of the acc_(k) to rationals of denominator {tilde over (L)}, the cardinality of the set of possible w is at most {tilde over (L)}^(k−1). Moreover, by Lemma 46 in the appendix, the probability of the events {{circumflex over (p)}_(w,1,k)≧p_(1,k} is less than e) ^(−L) ^(π) ^((B−1)D(p) ^(1,k) ^(∥p) ^(1,k) ^(*)). So by the union bound the probability {{circumflex over (f)}_(k) ^(tot)≧f_(1,k)} is less than

({tilde over (L)})^(k−1) e ^(−L) ^(π) ^((B−1)D(p) ^(1,k) ^(∥p) ^(1,k) ^(*)).

Likewise, investigate the weighted proportion of correct decodings {circumflex over (q)}_(m) ^(tot) and the associated values {circumflex over (q)}_(1,k)=Σ_(j sent)π_(j)

which are compared to the target values q_(1,k) at steps k=1 to m. The event {{circumflex over (q)}_(1,k)<q_(1,k)} is contained in

_(k) so when bounding its

probability, incurring a cost of a factor of e^(kc) ⁰ , one may switch to the simpler measure

.

Consider the event A=∪_(k=1) ^(m)A_(k), where A_(k) is the union of the events {{circumflex over (q)}_(1,k)≦q_(1,k)}, {{circumflex over (f)}_(k) ^(tot)≧f_(1,k)} and {χ_(n−k+1) ²/n<1−h}. This event A may be decomposed as the union for k from 1 to m of the disjoint events A_(k)∩_(k′=1) ^(k−1)A_(k′) ^(c). The Chi-square event may be expressed as A_(h,k)={χ_(n−k+1) ²/(n−k+1)<1−h_(k)} which has the probability bound

^(−(n − k + 1)D_(h_(k))).

So to bound the probability of A, it remains to bound for k from 1 to m, the probability of the event

A _(q,k) ={{circumflex over (q)} _(1,k) <q _(1,k) }∩A _(h,k) ^(c)Ω_(k′=1) ^(k−1) A _(k′) ^(c).

In this event, with the intersection of A_(k′) ^(c) for all k′<k and the intersection with the Chi-square event A_(h,k) ^(c), the statistic

_(k,j) ^(comb) exceeds the corresponding approximation

√{square root over (s_(k))}√{square root over (C_(j,R,B,h))}1_(j sent) +Z _(ŵ) _(acc) _(,k,j) ^(comb),

where s_(k)=1/[1−q_(1,k−1) ^(adj)ν]. There is a finite set of possible ŵ^(acc) associated with the grid of values of acc₁, . . . , acc_(k−1) rounded to rationals of denominator {tilde over (L)}. Now A_(q,k) is contained in the union across possible w of the events

{q̂_(w, 1, k) < q_(1, k)} where ${\hat{q}}_{w,1,k} = {\sum\limits_{j\mspace{14mu} {sent}}\; {\pi_{j}{1_{\{{Z_{w,k,j}^{comb} \geq a_{k,j}}\}}.}}}$

Here a_(k,j)=τ−√{square root over (s_(k))}√{square root over (C_(j,R,B,h))}. With respect to

, these z_(w,k,j) ^(comb) are standard normal, independent across j, so the Bernoulli random variables 1_({Z) _(w,k,j) _(comb) _(≧a) _(k,j) _(}) have success probability Φ(a_(k,j)) and accordingly, with respect to

, the {circumflex over (q)}_(w,1,k) has expectation q_(1,k)*=Σ_(j sent)π_(j) Φ(a_(k,j)). Thus, again by Lemma 46 in the appendix the probability of

{{circumflex over (q)}_(w,1,k) <q _(1,k)}

is not more than

e ^(−L) ^(π) ^(D(q) ^(1,k) ^(∥q) ^(1,k) ^(*)).

By the union bound multiply this by ({tilde over (L)})^(k−1) to bound

(A_(q,k)). One may sum it across k to bound the probability of the union.

The Chi-square random variables and the normal statistics for j in other have the same distribution with respect to

and

so there is no need to multiply by the e^(c) ⁰ ^(k) factor for the A _(h) and A_(f) contributions.

The event of interest

A _(q) _(m) _(tot) ={{circumflex over (q)} _(m) ^(tot) ≦q _(1,m)}

is contained in the union of the event A_(q) _(m) _(tot) ∩A_(q,m−1) ^(c)∩A_(f) ^(c)∩A_(h) ^(c) with the events A_(q,m−1), A_(h) and A_(f), where A_(h)=∪_(k=1) ^(m)A_(h,k) and A_(f)=∪_(k=1) ^(m)A_(f,k). The three events A_(q,m−1), A_(h) and A_(f) are clearly part of the event A which has been shown to have the indicated exponential bound on its probability. This leaves us with the event

A _(q) _(m) _(tot) ∩A _(q,m−1) ^(c) ∩A _(f) ^(c) ∩A _(h) ^(c)

Now, as has been seen earlier herein, {circumflex over (q)}_(m) ^(tot) may be regarded as the weighted proportion of occurrence the union ∪_(k=1) ^(m)

_(k,j) which is at least Σ_(j sent)π_(j)

. Outside the exception sets A_(h), A_(f) and A_(q,m−1), it is at least {circumflex over (q)}_(1,m)=Σ_(j sent)π_(j)

. With the indicated intersections, the above event is contained in A_(q,m)={{circumflex over (q)}_(1,m)≦q_(1,m)}, which is also part of the event A. So by containment in a union of events for which we have the probability bounds, the indicated bound holds.

As a consequence of the above conclusion, outside the event A at step k=m, one has {circumflex over (q)}_(m) ^(tot)<q_(1,m). Thus outside A the weighted fraction of failed detections, which is not more than 1−{circumflex over (q)}_(1,m), is less than 1−q_(1,m). Also outside A, the weighted fraction of false alarms is less than f_(1,m). So the total weighted fraction of mistakes {circumflex over (δ)}_(m) is less than δ_(m)=(1−q_(1,m))+f_(1,m).

In these probability bounds the role in the exponent of D(q∥q*) for numbers q and q* in [0, 1], is played the relative entropy between the Bernoulli(q) and the Bernoulli q* distributions, even though these q and q* arise as expectations of weighted sums of many independent Bernoulli random variables.

Concerning the simplified bounds in the corollary, by the Pinsker-Csiszar-Kulback-Kemperman inequality, specialized to Bernoulli distributions, the expressions of the form D(q∥q*) in the above, exceed 2(q−q*)². This specialization gives rise to the e^(−2L) ^(π) ^(η) ² bound when the q_(1,k) and {tilde over (q)}_(1,k) differ from q_(1,k)* by the amount η.

The e^(−2L) ^(π) ^(η) ² bound arises alternatively by applying Hoeffding's inequality for sums of bounded independent random variables to the weighted combinations of Bernoulli random variables that arise with respect to the distribution

. As an aside, it is remarked that order η² is the proper characterization of D(q∥q*) only for the middle region of steps when q_(1,k)* is neither near 0 nor near 1. There are larger exponents toward the ends of the interval (0, 1) because Bernoulli random variables have less variability there.

To handle the exponents (B−1)D(p∥p*) at the small values p=p_(1,k)=f_(1,k)/(B−1) and p*=p_(1,k)*=f_(1,k)*(B−1), use the Poisson lower bound on the Bernoulli relative entropy, shown in the appendix. This produces the lower bound (B−1)[p_(1,k) log p_(1,k)/p_(1,k)*+p_(1,k)*−p_(1,k)] which is equal to

f _(1,k) log f _(1,k) /f _(1,k) *+f _(1,k) *−f _(1,k).

Write this value as f_(1,k)

(ρ_(k)) or equivalently f_(1,k)

(ρ_(k))/ρ_(k) where the functions

(ρ) and

(ρ)/ρ=log ρ+1−1/ρ are increasing in ρ≧1.

If one used f_(1,k)=kf and f_(1,k)*=kf* in fixed ratio ρ=f/f*, this lower bound on the exponent would be kf

(ρ)/ρ as small as f

(ρ)/ρ. Instead, keeping f_(1,k) locked at f, which is at least f*ρ, and keeping f_(1,k)*=kf* less than or equal to mf*= f*, the ratio ρ_(k) will be at least ρ and the exponents will be at least as large as f

(ρ)/ρ.

Finally, there is the matter of the refined exponent in II. As above proof the heart of the matter is the consideration of the probability

{{circumflex over (q)}_(w,1,k)<q_(1,k)}. Fix a value of k between 1 and m. Recall that {circumflex over (q)}_(w,1,k)=Σ_(j sent)π_(j)1_(H) _(w,k,j) . Bound the probability of the event that the stun of the independent random variables ξ_(j)=−π_(j)(1_(H) _(w,k,j) − Φ _(j)) exceeds η, where Φ _(j)= Φ(shift_(k,j)−τ)=

(H_(w,k,j)) provides the centering so that the ξ_(j) have mean 0. Recognize that Φ _(j) is Φ(μ_(j)(x))=1−Φ(μ_(j)(x)), evaluated at x=q_(1,k) ^(adj), and it is the same as used in the evaluation of the q_(1,k)*, the expected value of {circumflex over (q)}_(w,1,k), which is g_(L)(x). The random variables ε_(j) have magnitude bounded by max_(j)π_(j)=1/L_(π) and variance υ_(j)=π_(j) ²Φ_(j)(1−Φ_(j)). Thus bound

{{circumflex over (q)}_(w,1,k)<q_(1,k)} by Bernstein's inequality, where the stuns are understood to be for j in sent,

${{{\mathbb{Q}}\left\{ {{\sum\limits_{j}\; \xi_{j}} \geq \eta} \right\}} \leq {\exp \left\{ {- \frac{\eta^{2}}{2\left\lbrack {{V/L} + {\eta/\left( {3L_{\pi}} \right)}} \right\rbrack}} \right\}}},$

where here η=η_(k) is the difference between the mean q_(1,k)* and q_(1,k) and V/L=Σ_(j)υ_(j)=Σ_(j)π_(j) ²Φ_(j)(1−Φ_(j)) is the total variance. It is V_(k)/L given by

${{V(x)}/L} = {\sum\limits_{j}\; {\pi_{j}^{2}{\Phi \left( {\mu_{j}(x)} \right)}\left( {1 - {\Phi \left( {\mu_{j}(x)} \right)}} \right.}}$

evaluated at g_(1,k−1) ^(adj). This completes the demonstration of Theorem 8.

If one were to use the crude bound on the total variance of (max_(j)π_(j))Σ_(j)π_(j)¼=1/(4L_(π)) the result in II would be no better than the exp{−2L_(π)η²} bound that arises from the Hoeffding bound.

The variable power assignments to be studied arrange Φ_(j)(1−Φ_(j)) to be small for most j in sent. Indeed, a comparison of the stun V(x)/L to an integral, in a manner similar to the analysis of g_(L)(x) in an upcoming section, shows that V(x) is not more than a constant times 1/τ, which is of order 1/√{square root over (log B)}, by the calculation in Appendix 14.7. This produces, with a positive constant const, a bound of the form

exp{−constL min {η,η² √{square root over (log B)}}}.

Equivalently, in terms of n=(L log B)/R the exponent is at least a constant times n min{η²/√{square root over (log B)},η/log}. This exponential bound is an improvement on the other bounds in the Theorem 8, by a factor of √{square root over (log B)} in the exponent for a range of values of η up to 1/√{square root over (log B)}, provided of course that η<gap to permit the required increase in q_(1,k). For the best rates obtained here, η will need to be of order 1/log B, to within a log log factor, matching the order of

−R. So this improvement brings the exponent to within a √{square root over (log B)} factor of best possible.

Other bounds on the total variance are evident. For instance, from Σ_(j)π_(j)Φ_(j)(1−Φ_(j)) less than both Σ_(j)π_(j)Φ_(j) and Σ_(j)π_(j)(1−Φ_(j))}, it follows that

V(x)/L≦(1/L _(π))min{g _(L)(x),1−g _(L)(x)}.

This reveals that there is considerable improvement in the exponents provided by the Bernstein bound for the early and later steps where g_(L)(x) is near 0 or 1, even improving the order of the bounds there. This does not alter the fact that the decoder must experience the effect of the exponents for steps with x near the middle of the interval from 0 to 1, where the previously mentioned bound on V(x) produces an exponent of order Lη²√{square root over (log B)}.

For the above, data-driven weights λ are used, with which the error probability in a union bound had to be multiplied by a factor of {tilde over (L)}^(k−1), for each step k, to account for the size of the set of possible weight vectors.

Below a slight modification to the above procedure is described using deterministic λ that does away with this factor, thus demonstrating increased reliability for given rates below capacity. The procedure involves choosing each dec_(k) to be a subset of the terms above threshold, with the π weighted size of this set very near a pre-specified value pace_(k).

7.2 An Alternative Approach:

As mentioned earlier, instead of making dec_(k), the set of decoded terms for step k, to be equal to thresh_(k), one may take dec_(k) for each step to be a subset of thresh_(k) so that its size accept_(k) is near a deterministic quantity which called pace_(k). This will yield a sum accept_(k) ^(tot) near Σ_(k′=1) ^(k)pace_(k′) which is arranged to match q_(1,k). Again abbreviate accept_(k) ^(tot) as acc_(k) ^(tot) and accept_(k) as acc_(k).

In particular, setting pace_(k)=q_(1,k) ^(adj)−q_(1,k−1) ^(adj), the set dec_(k) is chosen by selecting terms in J_(k) that are above threshold, in decreasing order of their

_(k,j) ^(comb) values, until for each k the accumulated amount nearly equals q_(1,k). In particular given acc_(k−1) ^(tot), one continues to add terms to acc_(k), if possible, until their sum satisfies the following requirement,

q _(1,k) ^(adj)−1/L _(π) <acc _(k) ^(tot) ≦q _(1,k) ^(adj),

where recall that 1/L_(π) is the minimum weight among all j in J. It is a small term of order 1/L.

Of course the set of terms thresh_(k) might not be large enough to arrange for accept_(k) satisfying the above requirement. Nevertheless, it is satisfied, provided

${{acc}_{k - 1}^{tot} + {\sum\limits_{j \in {thresh}_{k}}\; \pi_{j}}} \geq q_{1,k}^{adj}$

or equivalently,

${{\sum\limits_{j \in {dec}_{1,{k - 1}}}\; \pi_{j}} + {\sum\limits_{j \in {J - {dec}_{1,{k - 1}}}}\; {\pi_{j}1_{\mathcal{H}_{k,j}}}}} \geq {q_{1,k}^{adj}.}$

Here for convenience take dec₀=dec_(1,0) as the empty set.

To demonstrate satisfaction of this condition note that the left side is at least the value one has if the indicator

is imposed for each j and if the one restricts to j in sent, which is the value {circumflex over (q)}_(1,k) ^(above)=Σ_(jεsent)

. Analysis for this case demonstrates, for each k, that the inequality

{circumflex over (q)} _(1,k) ^(above) >q _(1,k)

holds with high probability, which in turn exceeds q_(1,k) ^(adj). So then the above requirement is satisfied for each step, with high probability, and thence acc_(k) matches pacc_(k) to within 1/L_(π).

This {circumflex over (q)}_(1,k) ^(above) corresponds to the quantity studied in the previous section, giving the weighted total of terms in sent for which the combined statistic is above threshold, and it remains likely that it exceeds the purified statistic {circumflex over (q)}_(1,k). What is different is the control on the size of the previously decoded sets allows for constant weights of combination.

In the previous procedure random weights ŵ_(k) ^(acc) were employed in the assignment of the λ_(1,k), λ_(2,k), . . . , λ_(k,k) used in the definition of

_(k,j) ^(comb) and Z_(k,j) ^(comb), where recall that ŵ_(k) ^(acc)=1/(1−acc_(k−1) ^(tot)ν)−1/(1−acc_(k−2) ^(tot)ν). Here, since each acc_(j) ^(tot) is near a deterministic quantity, namely q_(1,k) ^(adj), replace ŵ_(k) ^(acc) by a deterministic quantity w_(k)* given by,

${w_{k}^{*} = {\frac{1}{\left( {1 - {q_{1,{k - 1}}^{adj}v}} \right)} - \frac{1}{\left( {1 - {q_{1,{k - 2}}^{adj}v}} \right)}}},$

and use the corresponding vector λ* with coordinates λ_(k′,k)*=w_(k′)*/[1+w₂*+ . . . +w_(k)*] for k′=1 to k.

Earlier the inequality ŵ_(k)≦ŵ_(k) ^(acc) was demonstrated, which allowed quantification of the shift factor in each step. Analogously, the following result is obtained for the current procedure using deterministic weights.

Lemma 10.

For k′<k, assume the decoding sets dec_(1,k′) are arranged so that the corresponding acc_(k′) ^(tot) takes value in the interval (q_(1,k′) ^(adj)−1/L_(π),q_(1,k′) ^(adh)]. Then

ŵ _(k) ≦w _(k)*+ε₁,

where ε₁=ν/(L_(π)(1−ν)²)=snr(1+snr)/L_(π) is a small term of order 1/L. Likewise. ŵ_(k′)≦w_(k)*+ε₂ holds for k′<k as well.

Demonstration of Lemma 10:

The {circumflex over (q)}_(k′) and {circumflex over (f)}_(k′) are the weighted sizes of the sets of true terms and false alarms, respectively, retaining that which is actually decoded on step k′, not merely above threshold. These have sum {circumflex over (δ)}_(k′)+{circumflex over (f)}_(k′)=acc_(k′), nearly equal to pace_(k′), taken here to be q_(1,k′) ^(adj)−q_(1,k′−1) ^(adj). Let's establish the inequalities

{circumflex over (q)} ₁ ^(adj)+ . . . +{circumflex over (q)}_(k−1) ^(adj) ≦q _(1,k−1) ^(adj)

and

{circumflex over (q)} _(k−1) ^(adj) ≦q _(1,k−1) ^(adj) −q _(1,k−2) ^(adj)+1/L _(π).

The first inequality uses that each {circumflex over (q)}_(k′) ^(adj) is not more than {circumflex over (q)}_(k), which is not more than {circumflex over (q)}_(k′)+{circumflex over (f)}_(k′), equal to acc_(k′) which sums to acc^(k−1) ^(tot) not more than q_(1,k−1) ^(adj). The second inequality is a consequence of the fact that {circumflex over (q)}_(k−1) ^(adj)≦acc_(k−1) ^(tot)−acc_(k−2) ^(tot). Using the bounds on acc_(k−1) ^(tot) and acc_(k−2) ^(tot) gives that claimed inequality.

These two inequalities yield

${\hat{w}}_{k} \leq {\frac{\left( {q_{1,{k - 1}}^{adj} - q_{1,{k - 2}}^{adj} + {1/L_{\pi}}} \right)v}{\left( {1 - {q_{1,{k - 1}}^{adj}v}} \right)\left( {1 - {q_{1,{k - 2}}^{adj}v}} \right)}.}$

The right side can be written as,

$\frac{1}{1 - {q_{1,{k - 1}}^{adj}v}} - \frac{1}{1 - {q_{1,{k - 2}}^{adj}v}} + {\frac{{1/L_{\pi}}v}{\left( {1 - {q_{1,{k - 1}}^{adj}v}} \right)\left( {1 - {q_{1,{k - 2}}^{adj}v}} \right)}.}$

Now bound the last term using q_(1,k−1) ^(adj) and q_(1,k−2) ^(adj) less than 1 to complete the demonstration of Lemma 10.

Define the exception set A_(q,above)=∪_(k′=1) ^(k−1){{circumflex over (q)}_(1,k′) ^(above)<q_(1,k′)}. In some expressions above is abbreviated as abv. Also recall the set A_(f)=∪k′=1 ^(k−1){{circumflex over (f)}_(k′) ^(tot)>f_(1,k′)}. For convenience suppress the dependence on k in these sets.

Outside of A_(q,adv), the {circumflex over (q)}_(1,k′) ^(abv) at least q_(1,k′) and hence at least q_(1,k) ^(adj) for each 1≦k′<k, ensuring that for each such k′ one can get decoding sets dec_(k′) such that the corresponding acc_(k′) ^(tot) is at most 1/L_(π) below q_(1,k′) ^(adj). Thus the requirements of Lemma 10 are satisfied outside this set.

Now proceed to lower bound the shift factor for step k outside of A_(q,abv)∪A_(f).

For the above choice of λ=λ* the shift factor is equal to the ratio

$\frac{1 + \sqrt{{\hat{w}}_{2}w_{2}^{*}} + \ldots + \sqrt{{\hat{w}}_{k}w_{k}^{*}}}{\sqrt{1 + w_{2}^{*} + \ldots + w_{k}^{*}}}.$

Using the above lemma and the fact that √{square root over (a−b)}≧√{square root over (a)}−√{square root over (b)}, obtain that the above is greater than or equal to

$\frac{1 + {\hat{w}}_{2} + \ldots + {\hat{w}}_{k}}{\sqrt{1 + w_{2}^{*} + \ldots + w_{k}^{*}}} - {\sqrt{\varepsilon_{1}}{\frac{\sqrt{{\hat{w}}_{2}} + \ldots + \sqrt{{\hat{w}}_{k}}}{\sqrt{1 + w_{2}^{*} + \ldots + w_{k}^{*}}}.}}$

Now use the fact that

√{square root over (ŵ₂)}+ . . . +√{square root over (ŵ_(k))}≦√{square root over (k)}√{square root over (ŵ₂+ . . . +ŵ_(k))}

to bound the second term by ε₂=√{square root over (ε₁)}√{square root over (k)}√{square root over (ν/(1−ν))} which is snr√{square root over ((1−snr)k/L_(π))}, a term of order near 1/√{square root over (L)}. Hence the shift factor is at least,

$\frac{1 + {\hat{w}}_{2} + \ldots + {\hat{w}}_{k}}{\sqrt{1 + w_{2} + \ldots + w_{k}}} - {\varepsilon_{2}.}$

Consequently, it is at least

$\frac{\sqrt{1 - {q_{1,{k - 1}}^{adj}v}}}{1 - {{\hat{q}}_{k - 1}^{{tot},{adj}}v}} - {\varepsilon_{2}.}$

where recall that {circumflex over (q)}_(k−1) ^(tot,adhj)={circumflex over (q)}_(k−1) ^(tot)/(1+{circumflex over (f)}_(k−1) ^(tot)/{circumflex over (q)}_(k−1) ^(tot)). Here it is used that 1+ŵ₂+ . . . +ŵ_(k), which is 1/(1−{circumflex over (q)}_(k−1) ^(adj,tot)ν), can be bounded from below by 1/(1−{circumflex over (q)}_(k−1) ^(tot,adj)ν) using Lemma 4.

Similar to before, note that q_(1,k−1) ^(adj) and q_(k−1) ^(tot,adj) are close to each other when the false alarm effects are small. Hence write this shift factor in the form

$\sqrt{\frac{1 - {\hat{h}}_{f,{k - 1}}}{1 - {{\hat{q}}_{k - 1}^{{tot},{adj}}v}}}$

as before. Again find that

ĥ _(f,k−1)≦{circumflex over (2)}f _(k−1) ^(tot) snr+ε ₃

outside of the exception set A_(q,abv). Here

${c_{3} = {\frac{snr}{L_{\pi}} + {2\; c_{2}}}},$

is a term of order 1/√{square root over (L)}.

To confirm the above use the inequality √{square root over (1−a)}−√{square root over (b)}≧√{square root over (1−c)}, where c=a+2√{square root over (b)}. Here a=(q_(1,k−1)−{circumflex over (q)}_(k−1) ^(tot,adj))ν/(1−{circumflex over (q)}_(k−1) ^(tot,adj)ν) and b=ε₂ ²(1−q_(k−1) ^(tot,adj)ν). Noting that the numerator in a is at most (1/L_(π)+2{circumflex over (f)}_(k−1) ^(tot)−({circumflex over (f)}_(k−1) ^(tot))²)ν outside of A_(q,abv) and that 0≦q_(k−) ^(tot,adj)≦1, one obtains the bound for ĥ_(f,k−1).

Next, recall outside of the exception set A_(f)∪A_(q,abv) that {circumflex over (q)}_(k−1) ^(tot)≧q_(1,k−1) and {circumflex over (f)}_(k−1) ^(tot)≦f_(1,k−1). This leads to the shift factor being at least

$\sqrt{\frac{1 - h_{f,{k - 1}}}{1 - {q_{1,{k - 1}}^{adj}v}}},{where}$ h_(f, k) = 2 f_(1, k)snr + ε₃.

As before, assume a bound f_(1,k)≦ f, so that h_(f,k) is not more than h_(f)=2 fsnr+ε₃, independent of k.

As done herein previously, create the combined statistics

_(k,j) ^(comb), now using the deterministic λ*. For j in other this

_(k,j) ^(comb) equals Z_(k,j) ^(comb) and for j in sent, when outside the exception set A_(abv)=A_(q,abv)∪A_(f)∪A_(h), this combination exceeds

${{\sqrt{\frac{1 - h^{\prime}}{1 - {q_{1,{k - 1}}^{adj}v}}}\sqrt{C_{j,R,B}}1_{j\mspace{14mu} {sent}}} + Z_{k,j}^{comb}},$

where (1−h′)=(1−h)(1−j_(f)) as before, though with h_(f) larger by the small amount ε₃. Again obtain shift_(k,j)=√{square root over (C_(j,R,B,h/()1−xν))} evaluated at x=q_(1,k−1) ^(adj), with C_(j,R,B,h) as before.

Analogous to Theorem 8, reliability after m steps of the decoder is demonstrated by bounding the probability of the exception set A=∪_(k−1) ^(m)A_(k), where A_(k) is the union of the events {{circumflex over (q)}_(1,k) ^(abv)≦q_(1,k)}, {{circumflex over (k)}_(k) ^(tot)≧f_(1,k)} and {χ_(n−k+1) ²/n<1−h}. Thus the proof of Theorem 8 carries over, only now it is not required to take the union over the grid of values of the weights. The analogous theorem with the resulting improved bounds is now stated.

Theorem 11.

Under the same assumptions as in Theorem 8, the Tit step adaptive successive decoder, using deterministic pacing with pace_(k)=q_(1,k)−q_(1,k−1), incurs a weighted fraction of errors {circumflex over (δ)}_(m) less than δ_(m)=f_(1,m)+(1−q_(1,m)), except in an event of probability not more than

${{\sum\limits_{k = 1}^{m}\; \left\lbrack ^{{{{- L_{\pi}}{D{({q_{1,k}{q_{1,k}^{*}}})}}})} + {c_{0}k}} \right\rbrack} + {\sum\limits_{k = 1}^{m}\; \left\lbrack ^{{- {L_{\pi}{({B - 1})}}}{D{({p_{1,k}{p_{1,k}^{*}}})}}} \right\rbrack} + {\sum\limits_{k = 1}^{m}\; ^{{- {({n - k + 1})}}D_{h_{k}}}}},$

where the bound also holds if the exponent L_(π)D(q_(1,k)∥q_(1,k)*) is replaced by

$L{\frac{\eta_{k}^{2}}{V_{k} + {\left( {1/3} \right){\eta_{k}\left( {L/L_{\pi}} \right)}}}.}$

In the constant gap bound case, with positive η and f and m≧2, satisfying the same hypotheses as in the previous corollary, the probability of {circumflex over (δ)}_(m) greater than δ*+η+ f is not more than

me ^(−2L) ^(π) ^(η) ² ^(+mc) ⁰ +

+me^(−(n−m+1)h) ^(m) ² ^(/2).

Furthermore, using the variance V_(k) and allowing a variable gap bound gap_(k)≦g_(L)(x_(k))−x_(k) and 0<f_(1,k)+η_(k)<gap_(k), with difference gap′=gap_(k)−f_(1,k)+η_(k) and number of steps m≦1+1/gap′, and with ρ_(k)=f_(1,k)/f_(1,k)*<1, this probability bound also holds with the exponent

$L\mspace{14mu} {\min\limits_{k}{\eta_{k}^{2}/\left\lbrack {V_{k} + {\left( {1/3} \right){\eta_{k}\left( {L/L_{\pi}} \right)}}} \right\rbrack}}$

in place of 2L_(π)η² and with min_(k)f_(1,k)

(ρ_(k))/ρ_(k) in place of f

(ρ)/ρ, where the minima are taken over k from 1 to m.

The bounds are the same as in Theorem 9 and its corollary, except for improvement due to the absence of the factors {tilde over (L)}^(k−1). In the same manner as discussed there, there are choices of f, ρ and h, such that the exponents for the false alarms and the chi-square contributions are at least as good as for the q_(1,k), so that the bound becomes

3me ^(−2L) ^(π) ^(η) ² ^(+mc) ⁰ .

It is remarked that for the particular variable power allocation rule studied in the upcoming sections, as said, the update function g_(L)(x) will seen to be ultimately insensitive to L, with g_(L)(x)−x rapidly approaching a function g(x)−x at rate 1/L uniformly in x. Indeed, a gap bound for g_(L) will be seen to take a form gap_(L)=gap*−θ/L_(π) for some constant θ, so that it approaches the value of the gap determined by g, denoted gap*, where note that L and L_(π) agree to within a constant factor. Accordingly, using gap*−θ/L_(π) in apportioning the values of η, f, and 1/(m−1), these values are likewise ultimately insensitive to L. Indeed, slight adjustment to the rate allows arrangement of a gap independent of L.

Nevertheless, to see if there be any effect on the exponent, suppose for a specified η* that η=η*−θ/L_(π) represents a corresponding reduction in η due to finite L. Consider the exponential bound

e ^(−2L) ^(π) ^(η) ² .

Expanding the square it is seen that the exponent L_(π)η², which is L_(π)(η*−θ/L_(π))², is at least L_(π)(η*)² minus a term 2θη* that is negligible in comparison. Thus the approach of η to η* is sufficiently rapid that the probability bound remains close to what it would be,

e ^(−2L) ^(π) ^((ƒ*)) ² ,

if one were to ignore the effect of the θ/L_(π), where it is used that L_(π)(η*)² is large, and that η* is small, e.g., of the order of 1/log B.

8 Computational Illustrations

An important part of the invention herein is a device for evaluating the performance of a decoder depending on the parameters of the design, including L, B, a snr, the choice of power allocations, and the amount that the rate is below capacity. The heart of this device is the successive evaluation of the update function g_(L)(x). Accordingly, the performance of the decoder is illustrated. First, for fixed values of the design parameters and rates below capacity, evaluate the detection rate as well as the probability of the exception set P_(ε) using the theoretical bounds given in Theorem 11. Plots demonstrating the progression of the decoder are also shown in specific figures. These highlight the crucial role of the function g_(L) in achieving performance objectives.

FIGS. 4, 5, and 6 presents the results of computation using the reliability bounds of Theorem 11 for fixed L and B and various choices of snr and rates below capacity. The dots in these figures denotes q_(1,k) ^(adj) for each k and the step function joining these dots highlight how q_(1,k) ^(adj) is computed from q_(1,k−1) ^(adj). For large L these q_(1,k) ^(adj)'s would be near q_(1,k), the lower bound on the proportion of sections decoded after k passes. In this extreme case q_(1,k), would match g_(L)(q_(1,k−1)), so that the dots would lie on the function.

For illustrative purposes take B=2¹⁶, L=B and snr values of 1, 7 and 15, in these three figures. For each snr value the maximum rate, over a grid of values, is determined, for which there is a particular control on the error probability. With snr=1 (FIG. 6), this rate R is 0.3 bits which is 593/4 of capacity. When snr is 7 and 15 (FIGS. 4 and 5), respectively, these rates correspond to 49.5% and 43.5% of their corresponding capacities.

Specifically, for FIG. 5, with snr=15, the variable power allocation was used with P_((l)) proportional to e^(−2Cl/L) and a=1.3375. The weighted (unweighted) detection rate is 0.995 (0.983) for a failed detection rate of 0.017 and the false alarm rate is 0.006. The probability of mistakes larger than these targets is bounded by 5.4×10⁻⁴.

For FIG. 6, with snr=1, constant power allocation was used for the sections with a=0.6625. The detection rate (both weighted and un-weighted) is 0.944 and the false alarm and failed detection rates are 0.016 and 0.056 respectively, with the corresponding error probability bounded by 2.1×10⁻⁴.

Specifics for FIG. 4 with snr=7 were already discussed in the introduction.

The error probability in these calculations is controlled as follows. Arrange each of the 3m terms in the probability bound to take the same value, set in these examples to be ε=10⁻⁵. In particular, compute in succession appropriate values of q_(1,k)* and f_(1,k)*=kf*, using an evaluation of the function g_(L)(x), an L term sum, evaluated at a point determined from the previous step, and from these determine q_(1,k) and f_(1,k).

This means solving for q_(1,k) less than q_(1,k)* such that e^(−L) ^(π) ^(D(q) ^(1,k) ^(∥q) ^(1,k) ^(*)+c) ⁰ ^(k) equals ε, and with p_(1,k)*=f_(1,k)*/(B−1), solving for the p_(1,k) greater than p_(1,k)* such that the corresponding term e^(−L) ^(π) ^((B−1)D(p) ^(1,k) ^(∥p) ^(1,k) ^(*)) also equals ε. In this way, the largest q_(1,k) less than q_(1,k)* is used, that is, the smallest η_(k), and the smallest false alarm bound f_(1,k), for which the respective contributions to the error probability bound is not worse then the prescribed value.

These are numerically simple to solve because D(q∥q*) is convex and monotone in q<q*, and likewise for D(p∥p*) for p>p*. Likewise arrange h_(k) so that e^(−(n−k+1)D) ^(h) ^(k) matches ε.

Taking advantage of the Bernstein bound sometimes yields a smaller η_(k) by solving for the choice satisfying the quadratic equation Lη_(k) ²/[V_(k)+(⅓)η_(k)L/L_(π)]=log 1/ε+c₀k, where V_(k) is computed by an evaluation of V(x), which like q_(L)(x) is a L term sum, both of which are evaluated at x=q_(1,k−1) ^(adj).

These computation steps continue as long as (1−q_(1,k))+f_(1,k) decreases, thus yielding the choice of the number of steps m.

For these computations choose power allocations proportional to

max{e ^(−2γ(l−1)/L) ,e ^(−2γ)(1+δ_(c))},

with 0≦γ≦

. Here the choices of a, c and γ are made, by computational search, to minimize the resulting sum of false alarms and failed detections, per the bounds. In the snr=1 case the optimum γ is 0, so there is constant power allocation in this case. In the other two cases, there is variable power across most of the sections. The role of a positive c being to increase the relative power allocation for sections with low weights. Note, in the analytical results for maximum achievable rates as a function of B as given in the upcoming sections, γ is constrained to be equal to

, though the methodology does extend to the case of γ<

.

FIGS. 7,8,9 give plots of achievable rates as a function of B for snr values of 15, 7 and 1. The section error rate is controlled to be between 9 and 10%. For the curve using simulation runs the rates are exhibited for which the empirical probability of making more than 10% section mistakes is near 10⁻³.

For each B, the points on the detailed envelope correspond to the numerically evaluated maximum inner code rate for which the section mistake rate is between 9 and 10%. Here assume L to be large, so that the q_(1,k)'s and f_(k)'s are replaced by the expected values q_(1,k)* and f_(k)*, respectively. Also take h=0. This gives an idea about the best possible rates for a given snr and section mistake rate.

For the simulation curve, L was fixed at 100 and for given snr. B and rate values, 10⁴ runs of our decoder were performed. The maximum rate over the grid of values satisfying section error rate of less than 10% except in 10 replicates, (corresponding to an estimated P_(ε) of 10⁻³) are shown in the plots. Interestingly, even for such small values of L the curve is quite close to the detailed envelope curve, showing that the theoretical bounds herein are quite conservative.

9 Accumulative g

This section complements the previous computational results and reliability results to analytically quantify, in the finite L and B case, conditions on the rate moderately close to capacity, such that the update function g_(L)(x) is indeed accumulative for a suitable positive gap and an x* near 1.

In particular, normalized power allocation weights π_((l)) are developed in subsection 1, including slight modification to the exponential form. An integral approximation g(x) to the sum g_(L)(x) is provided in subsection 2. Subsection 3 examines the behavior of g_(L)(x) for x near 1, including introduction of x* via a parameter r₁ related to an amount of permitted rate drop and a parameter ζ related to the amount of shift at x*. For cases with monotone decreasing g(x)−X₇ as in the unmodified weight case, the behavior for x near 1 suffices to demonstrate that g_(L)(x) is accumulative. Improved closeness of the rate to capacity is shown in the finite codelength case by allowance of the modifications to the weight via the parameter δ_(c). But with this modifications monotonicity is lost. In Subsection 4, a bound on the number of oscillations of g(x)−x is established in that is used in showing that g_(L)(x) is accumulative. The location of x* and the value of δ_(c) both impact the mistake rate δ_(mis) and the amount of rate drop required for g_(L)(x) to be accumulative, expressed through a quantity introduced there denoted r_(crit). Subsection 5 provides optimization of δ_(c). Helpful inequalities in controlling the rate drop are in subsection 6. Subsection 7 provides optimization of the contribution to the total rate drop of the choice of location of x*, via optimization of ζ.

Recall that g_(L)(x) for 0≦x≦1 is the function given by

${{g_{L}(x)} = {\sum\limits_{l = 1}^{L}\; {\pi_{(l)}{\Phi \left( {{\mu_{l}(x)} - \tau} \right)}}}},$

where here denote μ_(l)(x)=shift_(l,x)=√{square root over (C_(l,R,B,h)/(1−xν))}. Recursively, q_(1,k) is obtained from q_(1,k)*=g_(L)(x) evaluated at x=q_(1,k−1) ^(adj), in succession for k from 1 to m.

The values of Φ(μ_(l)(x)−τ), as a function of l from 1 to L, provide what is interpreted as the probability with which the term sent from section l have approximate test statistic value that is above threshold, when the previous step successfully had an adjusted weighted fraction above threshold equal to x. The Φ(μ_(l)(x)−τ) is increasing in x regardless of the choice of π_(l), though how high is reached depends on the choice of this power allocation.

9.1 Variable Power Allocations:

Consider two closely related schemes for allocating the power. First suppose P_((l)) is proportional to

as motivated in the introduction. Then the weight for section l is π_((l)) given by P_((l))/P. In this case recall that C_(l,R)=π_((l))Lν/(2R) simplifies to u_(l) times the constant

/R where

u _(l)=

for sections l from 1 to L. The presence of the factor

/R if at least 1, increases the value of g_(L)(x) above what it would be if that factor were not there and helps in establishing that it is accumulative.

As l varies from 1 to L the ranges from 1 down to the value

=1−ν.

To roughly explain the behavior, as shall be see, this choice of power allocation produces values of Φ(μ_(l)(x)−τ) that are near 1 for l with u_(l) enough less than 1−xν and near 0 for values of u_(l) enough greater than 1−xν, with a region of l in between, in which there will be a scatter of sections with statistics above threshold. Though it is roughly successful in reaching an x near 1, the fraction of detections is limited, if R is too close to

, by the fact that μ_(l)(x) is not large for a portion of l near the right end, of the order 1/√{square root over (2 log B)}.

Therefore, the power allocation is modified, taking π_((l)) to be proportional to an expression that is equal

$u_{l} = {\exp \left\{ {{- 2}\; C\frac{l - 1}{L}} \right\}}$

except for large l/L where it is leveled to be not less than a value u_(cut)=

(1+δ_(c)) which exceeds (1−ν)=

=1/(1+snr) using a small positive δ_(c). This δ_(c) is constrained to be between 0 and snr so that u_(cut) is not more than 1. Thus let π_((l)) be proportional to ũ_(l) given by

max{u _(l) ,u _(cut)}.

The idea is that by leveling the height to a slightly larger value for l/L near 1, nearly all sections are arranged to have ũ_(l) above (1−xν) when x is near 1. This will allow to reach the objective with an R closer to

. The required normalization could adversely affect the rate, but it will be seen to be of a smaller order of 1/(2 log B).

To produce the normalized π_((l)=max{u) _(l), u_(cut)} (L SUM compute

${sum} = {\sum\limits_{l = 1}^{L}\; {\max \left\{ {u_{l},u_{cut}} \right\} {\left( {1/L} \right).}}}$

If c=0 this sum equals ν/(2

) as previously seen. If c>0 and u_(cut)<1, it is the stun of two parts, depending on whether

is greater than or not greater than u_(cut). This sum can be computed exactly, but to produce a simplified expression let's note that replacing the sum by the corresponding integral

integ=∫₀ ¹max{

,u_(cut) }dt

an error of at most 1/L is incurred. For each L there is a θ with 0≦θ≦1 such that

sum=integ+θ/L.

In the integral,

to u_(cut) corresponds to comparing corresponds to comparing t to t_(cut) equal to [1/(2

)] log 1/u_(cut). Splitting the integral accordingly, it is seen to equal [1/(2

)](1−u_(cut)) plus u_(cut)(1−t_(cut)), which may be expressed as

${{integ} = {\frac{v}{2\; C}\left\lbrack {1 + {{D\left( \delta_{c} \right)}/{snr}}} \right\rbrack}},$

where D(δ)=(1+δ)log(1+δ)−δ. For δ≧0, the function D(δ) is not more than δ²/2, which is a tight bound for small δ. This [1+D(δ_(c))/snr] factor in the normalization, represents a cost to us of introduction of the otherwise helpful δ_(c). Nevertheless, this remainder D(δ_(c))/snr is small compared to δ_(c), when δ_(c) is small compared to the snr. It might appear that D(δ_(c))/snr could get large if snr were small, but, in fact, since δ_(c)≦snr the D(δ_(c))/snr remains less than snr/2.

Accordingly, from the above relationship to the integral, the sum may be expressed as

${{sum} = {\frac{v}{2\; C}\left\lbrack {1 + \delta_{sum}^{2}} \right\rbrack}},$

where δ_(sum) ² is equal to D(δ_(c))/snr+2θ

/(Lv), which is not more than δ_(c) ²/(2snr)+2

/(Lν). Thus

$\pi_{(l)} = {\frac{\max \left\{ {u_{l},u_{cut}} \right\}}{L\mspace{14mu} {sum}} = {\frac{2\; C}{Lv}{\frac{\max \left\{ {u_{l},u_{cut}} \right\}}{1 + \delta_{sum}^{2}}.}}}$

In this case C_(l,R,B,h)=(π_(l)Lν/(2R))(1−h′)(2 log B) may be written

${C_{l,R,B,h} = {\max \left\{ {u_{l},u_{cut}} \right\} \frac{C\left( {1 - h^{\prime}} \right)}{R\left( {1 + \delta_{sum}^{2}} \right)}\left( {2\; \log \; B} \right)}},$

or equivalently, using τ=√{square root over (2 log B)}(1+δ_(a)), this is

max {u_(l), u_(cut)}(C^(′)/R)τ², where $C^{\prime} = {\frac{C\left( {1 - h^{\prime}} \right)}{\left( {1 + \delta_{sum}^{2}} \right)\left( {1 + \delta_{a}} \right)^{2}}.}$

For small δ_(c), δ_(a), and h′ this is a value near the capacity

. As seen later, the best choices of these parameters make

less than capacity by an amount of order log log B/log B. When δ_(c)=0 the

/(1+δ_(sum) ²) is what is previously herein called

and its closeness to capacity is controlled by δ_(sum) ²≦2

/(νL).

In contrast, if δ_(c) were taken to be the maximum permitted, which is δ_(c)=snr, then the power allocation would revert to the constant allocation rule, with an exact match of the integral and the sum, so that 1+δ_(sum) ²=1+D(snr)/snr and the

/(1+δ_(sum) ²) simplifies to R₀=(½)snr/(1+snr), which, as said herein, is a rate target substantially inferior to

, unless the snr is small.

Now μ_(l)(x)−τ which is √{square root over (C_(l,R,B,h/()1−xν))}−τ may be written as the function

μ(x,u)=(√{square root over (u/(1−xν))}−1)τ

evaluated at u=max{u_(l), u_(cut)}(

/R). For later reference note that the u_(l)(x) here and hence g_(L)(x) both depend on x and the rate R only through the quantity (1−xν)R/

.

Note also that μ(x, u) is of order τ and whether it is positive or negative depends on whether or not u exceeds 1−xν in accordance with the discussion above.

9.2 Formulation and Evaluation of the Integral g (x):

The function that updates the target fraction of correct decodings is

${g_{L}(x)} = {\sum\limits_{l = 1}^{L}\; {\pi_{(l)}{\Phi \left( {{\mu_{l}(x)} - \tau} \right)}}}$

which, for the variable power allocation with allowance for leveling, takes the form

${\sum\limits_{l = 1}^{L}\; {\pi_{(l)}{\Phi \left( {\mu \left( {x,{\max \left\{ {u_{l},u_{cut}} \right\} {C^{\prime}/R}}} \right)} \right)}}},$

with

$u_{l} = {^{{- 2}\; C\frac{l - 1}{L}}.}$

From the above expression for π_((l)), this g_(L)(x) is equal to

$\frac{2\; C}{vL}{\sum\limits_{l = 1}^{L}\; {\frac{\max \left\{ {u_{l},u_{cut}} \right\}}{1 + \delta_{sum}^{2}}{{\Phi \left( {\mu \left( {x,{\max \left\{ {u_{l},u_{cut}} \right\} {C^{\prime}/R}}} \right)} \right)}.}}}$

Recognize that this sum corresponds closely to an integral. In each interval

$\frac{l - 1}{L} \leq t < \frac{l}{L}$

for l from 1 to L, one have

$^{{- 2}\; C\frac{l - 1}{L}}$

at least

Consequently, g_(L)(x) is greater than g_(num)(x)/(1+δ_(sum) ²) where the numerator g_(num)(x) is the integral

$\frac{2\; C}{v}{\int_{0}^{1}{\max \left\{ {^{{- 2}\; {Ct}},u_{cut}} \right\} {\Phi\left( \ {\mu \left( {x,{\max \left\{ {^{{- 2}\; {Ct}},u_{cut}} \right\} {C^{\prime}/R}}} \right)} \right)}{{t}.}}}$

Accordingly, the quantity of interest g_(L)(x) has value at least (integ/sum)g(x) where

${g(x)} = {\frac{g_{num}(x)}{1 + {{D\left( \delta_{c} \right)}/{snr}}}.}$

Using

$\frac{integ}{sum} = {1 - {\frac{2\theta \; C}{L\; \eta}\frac{1}{1 + \delta_{sum}^{2}}}}$

and using that g_(L)(x)≦1 and hence g_(num)(x)/(1+δ_(sum) ²)≦1 it follows that

g _(L)(x)≧g(x)−2

/(Lν).

The g_(L)(x) and g(x) are increasing functions of x on [0, 1].

Let's provide further characterization and evaluation of the integral g_(num)(x) for the variable power allocation. Let z_(x) ^(low)=μ(x,u_(cut)

/R) and z_(X) ^(max)=μ(x,

/R). These have z_(x) ^(low)≦z_(x) ^(max), with equality only in the constant power case (where u_(cut)=1). For emphasis, write out that z_(x)=z_(X) ^(low) takes the form

$z_{x} = {\left\lbrack {\frac{\sqrt{u_{cut}{C^{\prime}/R}}}{\sqrt{1 - {xv}}} - 1} \right\rbrack {\tau.}}$

Set u_(x)=1−xν.

Lemma 12.

Integral evaluation. The g_(num)(x) for has a representation as the integral with respect to the standard normal density φ(z) of the function that takes the value 1+D(δ_(c))/snr for z less than z_(x) ^(low), takes the value

$x + {\frac{u_{x}}{v}\left( {1 - {\frac{R}{C^{\prime}}\left( {1 + {z/\tau}} \right)^{2}}} \right)}$

for z between z_(x) ^(low) and z_(x) ^(max), and takes the value 0 for z greater than z_(x) ^(max). This yields g_(num)(x) is equal to

${{\left\lbrack {1 + {{D\left( \delta_{c} \right)}/{snr}}} \right\rbrack {\Phi \left( z_{x}^{low} \right)}} + {\left\lbrack {x + {\delta_{R}\frac{u_{x}}{v}}} \right\rbrack \left\lbrack {{\Phi \left( z_{x}^{\max} \right)} - {\Phi \left( z_{x}^{low} \right)}} \right\rbrack} + {\frac{2\; R}{C^{\prime}}\frac{u_{x}}{v}\frac{\left\lbrack {{\varphi \left( z_{x}^{\max} \right)} - {\varphi \left( z_{x}^{low} \right)}} \right\rbrack}{\tau}} + {\frac{R}{C^{\prime}}\frac{u_{x}}{v}\frac{\left\lbrack {{z_{x}^{\max}{\varphi \left( z_{x}^{\max} \right)}} - {z_{x}^{low}{\varphi \left( z_{x}^{low} \right)}}} \right\rbrack}{\tau^{2}}}},\mspace{20mu} {where}$ $\mspace{20mu} {\delta_{R} = {1 - {{\frac{R}{C^{\prime}}\left\lbrack {1 + {1/\tau^{2}}} \right\rbrack}.}}}$

This δ_(R) is non-negative if R≦

/(1+1/τ²).

In the constant power case, corresponding to u_(cut)=1, the conclusion is consistent with the simpler g(x)=Φ(z_(x)).

The integrand above has value near x+(1−R/

)u_(x)/ν, if z is not too far from 0. The heart of the matter for analysis in this section is that this value is at least x for rates R≦

.

Demonstration of Lemma 12:

By definition, the function g_(num)(x) is

${\frac{2\; C}{v}{\int_{0}^{1}{\max \left\{ {^{{- 2}\; {Ct}},u_{cut}} \right\} {\Phi\left( \ {\mu \left( {x,{\max \left\{ {^{{- 2}\; {Ct}},u_{cut}} \right\} {C^{\prime}/R}}} \right)} \right)}{t}}}},$

which is equal to the integral

$\frac{2\; C}{v}{\int_{0}^{t_{cut}}{^{{- 2}\; {Ct}}{\Phi\left( \ {\mu \left( {x,{^{{- 2}\; {Ct}}{C^{\prime}/R}}} \right)} \right)}{t}}}$

plus the expression

${\frac{2\; C}{v}\left( {1 - t_{cut}} \right)u_{cut}{\Phi \left( z_{x}^{low} \right)}},$

which can also be written as [δ_(c)D(δ_(c))]Φ(z_(x) ^(low)))/snr.

Change the variable of integration from t to u=

, to produce the simplified expression for the integral

$\frac{1}{v}{\int_{u_{cut}}^{1}{{\Phi \left( {\mu \left( {x,{{uC}^{\prime}/R}} \right)} \right)}\ {{u}.}}}$

Add and subtract the value Φ(z_(x) ^(low)) in the integral to write it as [(1−u_(cut))/ν]Φ(z_(x) ^(low)), which is [1−δ_(c)/snr]Φ(z_(x) ^(low)), plus the integral

$\frac{1}{v}{\int_{u_{cut}}^{1}{\left\lbrack {{\Phi \left( {\mu \left( {x,{{uC}^{\prime}/R}} \right)} \right)} - {\Phi \left( {\mu \left( {x,{u_{cut}{C^{\prime}/R}}} \right)} \right)}} \right\rbrack \ {{u}.}}}$

Now since

Φ(b)−Φ(a)=∫1_({a<z<b})φ(z)dz,

it follows that this integral equals)

∫∫1_({u) _(cut) _(≦u≦1})

φ(z)dzdu/ν.

Switch the order of integration. In the integral, the inequality z≦μ(x,u

/R) is the same as

u≦u _(x) R/

(1+z/τ) ²,

which exceeds u_(cut) for z greater than z_(x) ^(low). Here u_(x)=1−xν. This determines an interval of values of u. For z between z_(x) ^(low) and z_(x) ^(max) the length of this interval of values of u is equal to

1−(R/

)u _(x)(1+z/τ) ².

Using u_(x)=1−xν one sees that this interval length, when divided by ν, may be written as

${x + {\frac{u_{x}}{v}\left( {1 - {\frac{R}{C^{\prime}}\left( {1 + {z/\tau}} \right)^{2}}} \right)}},$

a quadratic function of z.

Integrate with respect to φ(z). The resulting value of g_(num)(x) may be expressed as

${{\left\lbrack {1 + {{D\left( \delta_{c} \right)}/{snr}}} \right\rbrack {\Phi \left( z_{x}^{low} \right)}} + {\frac{1}{v}{\int_{z_{x}^{low}}^{z_{x}^{\max}}{\left\lbrack {1 - {\left( {R/C^{\prime}} \right){u_{x}\left( {1 + {z/\tau}} \right)}^{2}}} \right\rbrack {\varphi (z)}\ {z}}}}},$

To evaluate, expand the square (1+z/τ)² in the integrand as 1+2z/τ+z²/τ². Multiply by φ(z) and integrate. For the term linear in z, use zφ(z)=−φ′(z) for which its integral is a difference in values of φ(z) at the two end points. Likewise, for the term involving z²=1+(z²−1), use (z²−1)=−(zφ(z))′ which integrates to a difference in values of zφ(z). Of course the constant multiples of φ(z) integrate to a difference in values of Φ(z). The result for the integral matches what is stated in the Lemma. This completes the demonstration of Lemma 12.

One sees that the integral g_(num)(x) may also be expressed as

${{\frac{1}{snr}\left\lbrack {\delta_{c} + {D\left( \delta_{c} \right)}} \right\rbrack}{\Phi \left( z_{x} \right)}} + {\frac{1}{v}{\int{\left\lbrack {1 - {\max \left\{ {{u_{x}\frac{R}{C^{\prime}}\left( {1 + {z/\tau}} \right)_{+}^{2}},u_{cut}} \right\}}} \right\rbrack_{+}{\varphi (z)}{{z}.}}}}$

To reconcile this form with the integral given in the Lemma one notes that the integrand here for z below z_(x) takes the form of a particular constant value times φ(z) which, when integrated, provides a contribution that adds to the term involving φ(z_(x)).

Corollary 13.

Derivative evaluation. The derivative g′_(num)(x) is equal to

${\frac{\tau}{2}\left( {1 + \frac{z_{x}}{\tau}} \right)^{3}{\varphi \left( z_{x} \right)}\frac{R}{C^{\prime}}{\log \left( {1 + \delta_{c}} \right)}} + {\int_{z_{x}}^{z_{x}^{\max}}{\frac{R}{C^{\prime}}\left( {1 + {z/\tau}} \right)^{2}{\varphi (z)}\ {{z}.}}}$

In particular if δ_(c)=0 the derivative g′(x) is

${\frac{R}{C^{\prime}}{\int_{z_{x}^{low}}^{z_{x}^{\max}}{\left( {1 + {z/\tau}} \right)^{2}{\varphi (z)}{z}}}},$

and then, if also R=

/(1+r/τ²) with r≧1, that is, if R≦

/(1+1/τ²), the difference g(x)−x is a decreasing function of x.

Demonstration:

Consider the last expression given for g_(num)(x). The part [(δ_(c)+D(δ_(c))]Φ(z_(x))/snr has derivative

z _(x)′[δ_(c) +D(δ_(c))]φ(z _(x))/snr.

Use (1+z_(x)/τ)=√{square root over (u_(cut)(C′/R)/(1−xν))}{square root over (u_(cut)(C′/R)/(1−xν))} to evaluate z_(x)′ as

$z_{x}^{\prime} = {\frac{v}{2}\frac{1}{\left( {1 - {xv}} \right)^{3/2}}\sqrt{u_{cut}{C^{\prime}/R}}\tau}$

and obtain that it is (ν/2)(1+z_(x)/τ)³τ/(u_(cut)

/R). So using u_(cut)=(1−ν)(1−δ_(c)) the z_(x)′ is equal to

$\frac{snr}{2}\left( {1 + {z_{x}/\tau}} \right)^{3}\frac{\tau}{\left( {1 + \delta_{c}} \right)}{\frac{R}{C^{\prime}}.}$

This using the form of D(δ_(c)) and simplifying, the derivative of this part of g_(num) is the first part of the expression stated in the Lemma.

As for the integral in the expression for g_(num), its integrand is continuous and piecewise differentiable in x, and the integral of its derivative is the second part of the expression in the Lemma. Direct evaluation confirms that it is the derivative of the integral.

In the δ_(c)=0 case, this derivative specializes to the indicated expression which is less than

${{\frac{R}{C^{\prime}}{\int_{- \infty}^{\infty}{\left( {1 + {z/\tau}} \right)^{2}{\varphi (z)}\ {z}}}} = {\frac{R}{C^{\prime}}\left\lbrack {1 + {1/\tau^{2}}} \right\rbrack}},$

which by the choice of R is less than 1. Then g(x)−x is decreasing as it has a negative derivative. This completes the demonstration of Corollary 13.

Corollary 14.

A lower bound. The g_(num)(x) is at least g_(low)(x) given by

${\left\lbrack {1 + {{D\left( \delta_{c} \right)}/{snr}}} \right\rbrack {\Phi \left( z_{x} \right)}} + {\frac{1}{v}{\int_{z_{x}^{low}}^{\infty}{\left\lbrack {1 - {\left( {R/C^{\prime}} \right){u_{x}\left( {1 + {z/\tau}} \right)}^{2}}} \right\rbrack {\varphi (z)}\ {{z}.}}}}$

It has the analogous integral characterization as given immediately preceding Corollary 13, but with removal of the miter positive part restriction. Moreover, the function g_(low)(x)−x may be expressed as

$\mspace{20mu} {{{{glow}(x)} - x} = {\left( {1 - {xv}} \right)\frac{R}{{vC}^{\prime}}\frac{A\left( z_{x} \right)}{\tau^{2}}}}$   where $\frac{A(z)}{\tau^{2}} = {{\frac{C^{\prime}}{R} - \left( {1 + {1/\tau^{2}}} \right) - \frac{{2{{\tau\varphi}(z)}} + {z\; {\varphi (z)}}}{\tau^{2}} + {\left\lbrack {1 + {1/\tau^{2}} - {\left( {1 - \Delta_{c}} \right)\left( {1 + {z/\tau}} \right)^{2}}} \right\rbrack {{\Phi (z)}.\mspace{20mu} {with}}\mspace{14mu} \Delta_{c}}} = {{\log \left( {1 + \delta_{c}} \right)}.}}$

Optionally, the expression for g_(low)(x)−x may be written entirely in terms of z=z_(x) by noting that

${\left( {1 - {xv}} \right)\frac{R}{{vC}^{\prime}}} = {\frac{\left( {1 + \delta_{c}} \right)}{{{snr}\left( {1 + {z/\tau}} \right)}^{2}}.}$

Demonstration: The integral expressions for g_(low)(x) are the same as for g_(num)(x) except that the upper end point of the integration extends beyond z_(x) ^(max), where the integrand is negative, i.e., the outer restriction to the positive part is removed. The lower bound conclusion follows from this negativity of the integrand above z_(x) ^(max). Evaluate g_(low)(x) as in the proof of Lemma 12, using for the upper end that Φ(z) tends to 1, while φ(z) and zφ(z) tend to 0 as z→∞, to obtain that g_(low)(x) is equal to

${\left\lbrack {1 + {{D\left( \delta_{c} \right)}/{snr}}} \right\rbrack {\Phi \left( z_{x} \right)}} + {\left\lbrack {x + {\delta_{R}\frac{u_{x}}{v}}} \right\rbrack \left\lbrack {1 - {\Phi \left( z_{x} \right)}} \right\rbrack} - {2\frac{R}{C^{\prime}}\frac{u_{x}}{v}\frac{\varphi \left( z_{x} \right)}{\tau}} - {\frac{R}{C^{\prime}}\frac{u_{x}}{v}{\frac{z_{x}{\varphi \left( z_{x} \right)}}{\tau^{2}}.}}$

Replace the x+δ_(R)u_(x)/ν with the equivalent expression (1/ν)[1−u_(x)(R/

)(1+1/τ²)]. Group together the terms that are multiplied by u_(x)(R/

)/ν to be part of A/τ². Among what is left is 1/ν. Adding and subtracting x, this 1/ν is x+u_(x)/ν which is x+[(u_(x)/ν)R/

][

/R]. This provides the x term and contributes the

/R term to A/τ².

It then remains to handle [1+D(δ_(c))/snr]Φ(z_(x))−(1/ν)Φ(z_(x)) which is −(1/snr)[1−D(δ_(c))]Φ(z_(x)). Multiplying and dividing it by

$\frac{v\; C^{\prime}}{u_{x}R} = \frac{{{snr}\left( {1 + {z/\tau}} \right)}^{2}}{1 + \delta_{c}}$

and then noting that (1−D(δ_(c)))/(1+δ_(c)) equals 1−Δ_(c), it provides the associated term of A/τ². This completes the demonstration of Corollary 14.

What is gained with this lower bound is simplification because the result depends only on z_(x)=z_(X) ^(low) and not also on z_(x) ^(max).

9.3 Values of g(x) Near 1:

From the expression for x in terms of z, when R is near

, the point z=0 corresponds to a value of x near 1−δ_(c)/snr. This relationship is used to establish reference values of x* and z* and to bound how close g(x*) is to 1.

A convenient choice of x* satisfies (1−x*ν)R=(1−ν)

. More flexible is to allow other values of x* by choosing it along with a value r₁ to satisfy the condition

(1−x*ν)R=(1−ν)

/(1+r ₁/τ²).

Also call the solution x=x_(up). When r₁ is positive the x* is increased. Negative r₁ is allowed as long as r₁>−τ², but keep r₁ small compared to τ so that x* remains near 1.

With the rate R taken to be not more than

, write it as

$R = {\frac{C^{\prime}}{\left( {1 + {r/\tau^{2}}} \right)}.}$

Lemma 15.

A value of x* near 1. Let R′=

/(1+r₁/τ²). For any rate R between R′/(1+snr) and R′, the x* as defined above is between 0 and 1 and satisfies

${1 - x^{*}} = {\frac{R^{\prime} - R}{R\; {snr}} = {\frac{r - r_{1}}{{snr}\left( {\tau^{2} + r_{1}} \right)} = {\left( {1 - {x^{*}v}} \right){\frac{r - r_{1}}{v\left( {\tau^{2} + r} \right)}.}}}}$

It is near 1 it R is near R′. The value of z_(x) at x*, denoted z*=ζ satisfies

(1+ζ/τ)²=(1+δ_(c))(1+r ₁/τ²).

This relationship has δ_(c) near 2ζ/τ, when and ζ and r₁ are small in comparison to τ. The δ_(c)τ and r₁ are arranged, usually both positive, and of the order Of a power of a logarithm of τ, just large enough that Φ(ζ)=1−Φ(ζ) contributes to a small shortfall, yet not so large that it overly impacts the rate.

Demonstration of Lemma 15:

The expression 1−xν may also be written (1−ν)+(1−x)ν. So the above condition may be written 1+(1−x*)snr−R′/R which yields the first two equalities. It also may be written (1−x*ν)=(1−ν)(1+r/τ²)/(1+r₁/τ²) which yields the third equality in that same line.

Next recall that z_(x)=μ(x,u_(cut)

/R) which is

-   -            Recalling that u_(cut)=(1−ν)(1+δ_(c)), at x* it is z*=ζ given by

ζ=(√{square root over ((1+δ_(c))(1+r ₁/τ²))}{square root over ((1+δ_(c))(1+r ₁/τ²))}−1)τ,

or, rearranging, express δ_(c) in terms of z*=ζ and r₁ via

1+δ_(c)=(1+ζ/τ)²/(1+r ₁/τ²),

which is the last claim. This completes the demonstration of Lemma 15.

Because of this relationship one may just as well arrange u_(cut) in the first place via ζ as

u _(cut)=(1−ν)(1+ζ/τ)²/(1−r ₁/τ²)

where suitable choices for ζ and r₁ will be found in an upcoming section. Also keep the δ_(c) formulation as it is handy in expressing the affect on normalization via D(δ_(c)).

Come now to the evaluation of g_(L)(x*) and its lower bound via g_(low)(x*). Since g_(L)(x) depends on x and R only via the expression (1−xν)R/

, the choice of x* such that this expression is fixed at (1−ν) implies that the value g_(L)(x*) is invariant to R, depending only on the remaining parameters snr, δ_(c)m r₁, τ and L. Naturally then, the same is true of the lower bound via g_(low)(x*) which depends only on snr, δ_(c), r₁ and τ.

Lemma 16.

The value g(x*) is near 1. For the variable power case with 0≦δ_(c)<snr, the shortfall expressed by 1−g(x*) is less than

${\delta^{*} = \frac{{2\tau \; {\varphi (\zeta)}} + {\zeta \; {\varphi (\zeta)}} + {rem}}{{{snr}\left( {\tau^{2} + r_{1}} \right)}\left\lbrack {1 + {{D\left( \delta_{c} \right)}/{snr}}} \right\rbrack}},$

independent of the rate R≦R′, where the remainder is given by

rem=[(τ² +r ₁)D(δ_(c))−(r ₁−1)] Φ(ζ).

Moreover, g_(L)(x*) has shortfall δ_(L)*=1−g_(L)(x*) not more than δ*+2

/(Lν). In the constant power case, corresponding to δ_(c)=snr, the shortfall is

δ*=1−Φ(ζ)= Φ(ζ).

Setting

$\zeta = \sqrt{2\; \log \frac{\tau}{d\sqrt{2\pi}}}$

with a constant d and τ>d√{square root over (2π)}, with δ_(c) small, this δ* is near 2d/(snr τ²), whereas, with δ_(c)=snr, using Φ(ζ)≦φ(ζ)/ζ, it is not more than d/(ζτ).

Demonstration of Lemma 16

Using the lower bound on g(x*), the shortfall has the lower bound

$\delta^{*} = \frac{g_{low}\left( x^{*} \right)}{1 + {{D\left( \delta_{c} \right)}/{snr}}}$

which equals

$\frac{1 - {g_{low}\left( x^{*} \right)} + {{D\left( \delta_{c} \right)}/{snr}}}{1 + {{D\left( \delta_{c} \right)}/{snr}}}.$

Use the formula for g_(low)(x*) in the proof of Lemma 14. For this evaluation note that at x=x* the expression a u_(x)R/(ν

) simplifies to 1/[snr(1+r₁/τ²)] and the expression x+δ_(R)u_(x)/ν becomes

$1 + {\frac{r_{1} - 1}{{snr}\left( {\tau^{2} + r_{1}} \right)}.}$

Consequently, g_(low)(x*) equals

$1 + {\frac{D\left( \delta_{c} \right)}{snr}{\Phi (\zeta)}} - {\frac{{2\tau \; {\varphi (\zeta)}} + {{\zeta\varphi}(\zeta)} - {\left( {r_{1} - 1} \right){\overset{\_}{\Phi}(\zeta)}}}{{snr}\left( {\tau^{2} + r_{1}} \right)}.}$

This yields an expression for 1−g_(low)(x*)+D(δ_(c))/snr equal to

${{- \frac{D\left( \delta_{c} \right)}{snr}}{\overset{\_}{\Phi}(\zeta)}} + {\frac{{\left( {{2\tau} + \zeta} \right){\varphi (\zeta)}} - {\left( {r_{1} - 1} \right){\overset{\_}{\Phi}(\zeta)}}}{{snr}\left( {\tau^{2} + r_{1}} \right)}.}$

Group the terms involving Φ(ζ) to recognize this equals [(2τ+ζ)φ(ζ)+rem]/[snr(τ²+r₁)]. Then dividing by the expression 1+D(δ_(c))/snr produces the claimed bound.

As for evaluation at the choice ζ=√{square root over (2 log τ/d√{square root over (2π)})}, this is the positive value for which φ(ζ)=d/τ, when τ≧d√{square root over (2π)}. It provides the main contribution with 2τφ(ζ)=2d. The ζφ(ζ) is then ζd/τ which is of order √{square root over (log τ)}/τ, small compared to the main contribution 2d.

For the remainder rein, using Φ(ζ)≦φ(ζ)/ζ and D(δ_(c))≦(δ_(c))²/2 near 2ζ²/τ², the τ²D(δ_(c)) Φ(ζ) (is near 2ζφ(ζ)=2ζb₀/τ, again of order √{square root over (log τ)}/τ.

For δ_(L)*=1−g_(L)(x*), using g_(L)(x)≧g(x)+2

/(Lν) yields δ_(L)*≦δ*+2

/(Lν).

For the constant power case use g_(L)(x*)=g(x*)=Φ(ζ) directly, rather than g_(low)(x*). It has δ*= Φ(ζ), which is not more than φ(ζ)/ζ. This completes the demonstration of Lemma 16.

Corollary 17.

Mistake bound. The likely bound on the weighted fraction of failed detections and false alarms δ_(L)*+η+ f, corresponds to an unweighted fraction of not more than

δ_(mis) =fac(δ_(L) *+η+ f )

where the factor

fac=snr(1+δ_(sum) ²)/[2

(1+δ_(c))].

In the variable power case the contribution δ_(mis,L)*=facδ_(L)* is not more than δ_(mis)*+(1/L)(1+snr)/(1+δ_(c)) with

${\delta_{mis}^{*} = \frac{{\left( {{2\tau}\; + \zeta} \right)\; {\varphi (\zeta)}} + {rem}}{2{C\left( {\tau + \zeta} \right)}^{2}}},$

while, in the constant power case δ_(c)=snr, the fac=1 and δ_(mis,L)* equals

δ_(mis)*= Φ(ζ)

Closely related to δ_(mis)* in the variable power case is the simplified form

δ_(mis,simp)*=[(2τ+ζ)φ(ζ)+rem]/2

τ²,

for which δ_(mis)*=δ_(mis,simp)*/(1+ζ/τ)².

Demonstration of Corollary 17:

Multiplying the weighted fraction by the factor 1/[L min₁π_((l))], which equals the given fac, provides the upper bound on the (unweighted) fraction of mistakes δ_(mis)=fac(δ_(L)*+η+ f). Now δ_(L)*=1−g_(L)(x) has the upper bound

$\frac{1 - {g_{low}\left( x^{*} \right)} + \delta_{sum}^{2}}{1 + \delta_{sum}^{2}}.$

Multiplying by fac yields δ_(mis,L)*=facδ_(L)* not more than

$\frac{1 - {g_{low}\left( x^{*} \right)} + \delta_{sum}^{2}}{\left( {2{C/{snr}}} \right)\left( {1 + \delta_{c}} \right)}.$

Recall that δ_(sum) ² exceeds D(δ_(c))/snr by not more than 2

/(Lν) and that 1−g_(low)(x*)+D(δ_(c))/snr is less than [(2τ+ζ)φ(ζ)+rem]/[snr(τ²+r₁)]. So this yields the δ_(mis,L)* bound

$\frac{{\left( {{2\tau} + \zeta} \right){\varphi (\zeta)}} + {rem}}{2{C\left( {\tau^{2} + r_{1}} \right)}\left( {1 + \delta_{c}} \right)} + {\frac{\left( {1 + {snr}} \right)}{\left( {1 + \delta_{c}} \right)}{\frac{1}{L}.}}$

Recognizing that the denominator product (τ²+r₁)(1+δ_(c)) simplifies to (τ+ζ)² establishes the claimed form of δ_(mis)*.

For the constant power case note that fac=1 so that δ_(mis,L)*=δ_(mis)* is then unchanged from δ*= Φ(ζ). This completes the demonstration of Corollary 17.

9.4 Showing g(x) is Greater than x:

This section shows that g_(L)(x) is accumulative, that is, it is at least x for the interval from 0 to x*, under certain conditions on r.

Start by noting the size of the gap at x=x*.

Lemma 18.

The gap at x*. With rate R=

/(1+r/τ²), the difference g(x*)−x* is at least

$\frac{r - r_{up}}{{snr}\left( {\tau^{2} + r_{1}} \right)} = {\left( {1 - {x^{*}v}} \right){\frac{r - r_{up}}{v\left( {\tau^{2} + r} \right)}.}}$

Here, 0≦δ_(c)<snr, with rem as given in Lemma 16.

$r_{up} = {r_{1} + \frac{{\left( {{2\tau} + \zeta} \right){\varphi (\zeta)}} + {rem}}{1 + {{D\left( \delta_{c} \right)}/{snr}}}}$

while, for δ_(c)=snr,

r _(up) =r ₁ +snr(τ² +r ₁) Φ(ζ),

which satisfies

$\frac{r_{up}}{\tau^{2}} = {\frac{\left( {1 + {{snr}\; {\overset{\_}{\Phi}(\zeta)}}} \right)\left( {1 + {\zeta/\tau}} \right)^{2}}{1 + {snr}} - 1.}$

Keep in mind that the rate target

depends on δ_(c). For small δ_(c) is near the capacity

, whereas for δ_(c)=snr it is near R₀>0.

If the upcoming gap properties permit, it is desirable to set r near r_(up). Then the factor in the denominator of the rate becomes near 1+r_(up)/τ². In some cases r_(up) is negative, permitting 1+r/τ² not more than 1.

It is reminded that r₁, ζ, and δ_(c) are related by

${1 + {r_{1}/\tau^{2}}} = {\frac{\left( {1 + {\zeta/\tau}} \right)^{2}}{1 + \delta_{c}}.}$

Demonstration of Lemma 18:

The gap at x* equals g(x*)−x*. This value is the difference of 1−x* and δ*=1−g(x*), for which the bounds of the previous two lemmas hold. Recalling that 1−x* equals (r−r₁)/[snr(τ²+r₁)], adjust the subtraction of r₁ to include in r_(up) what is needed to account for δ* to obtain the indicated expressions for g(x*)−x* and r_(up). Alternative expressions arise by using the relationship that r₁ has to the other parameters. This complete the demonstration of Lemma 18.

Positivity of this gap at x* entails r>r_(up), and positivity of x* requires snr(τ²+r₁)+r₁≧r. There is an interval of such r provided snr(τ²−r₁)>r_(up)−r₁.

For this next two corollaries, take the case that either δ_(c)=snr or δ_(c)=0, that is, either the power allocation is constant (completely level), or the power P_((l)) is proportional to

${u_{} = {\exp \left\{ {{- 2}C\frac{ - 1}{L}} \right\}}},$

unmodified (no leveling). The idea in both cases is to look for whether the minimum of the gap occurs at x* under stated conditions.

Corollary 19.

Positivity of g(x)−x with constant power. Suppose R=

/(1+r/τ²) where, with constant power, the

equals R₀(1−h′)/(1+δ_(a))², and suppose ντ≧2(1+r/τ²)√{square root over (2π)}. Suppose r−r_(up) is positive with r_(up) as given in Lemma 18, specific to this δ_(c)=snr case. If r≧0 and if r−r_(up) is less than ν(τ+ζ)²/2, then, for 0≦x≦x*, the difference g(x)−x is at least

${{gap} = {\frac{r - r_{up}}{{snr}\left( {\tau^{2} + r_{1}} \right)} = \frac{r - r_{up}}{{v\left( {\tau + \zeta} \right)}^{2}}}},$

Whereas if r_(up)<r≦0 and if also

${{r/\tau} \geq {- \sqrt{2\; {\log \left( {v\; {{\tau \left( {1 + {r/\tau^{2}}} \right)}/2}\sqrt{2\pi}} \right)}}}},$

then the gap g(x)−x on [0, x*] is at least

$\min {\left\{ {{{1/2} + {r/\left( {\tau \sqrt{2\pi}} \right)}},\frac{r - r_{up}}{{v\left( {\tau + \zeta} \right)}^{2}}} \right\}.}$

In the latter case the minimum occurs at the second expression when

r<r _(up)+ν(τ+ζ)²[½+r/τ√{square root over (2π)}].

This corollary is proven in the appendix, where, under the stated conditions, it is shown that g(x)−x is unimodal for x≧0, so the value is smallest at x=0 or x=x*.

From the formula for r_(up) in this constant power case, it is negative, near −ντ², when snr Φ(ζ) and ζ/τ are small. It is tempting to try to set r close to r_(up), similarly negative. As discussed in the appendix, the conditions prevent pushing r too negative and compromise choices are available. With ντ at least a little more than the constant 2√{square root over (2π)}e^(π/4), allow r with which the 1+r/τ² factor becomes at best near 1−√{square root over (2π)}/2τ, indeed nice that it is not more than 1, though not as ambitious as the unobtainable 1+r_(up)/τ² near 1−ν.

Corollary 20.

Positivity of g(x)−x with no leveling. Suppose R=

/(1−r/τ²)], where, with δ_(c)=0, the

equals

(1h′)/(1+2

/νL)(1+δ_(a))² near capacity. Set r₁=0 and ζ=0 for which 1−x*=r/(snr τ²) and r_(up)=2τ/√{square root over (2π)}+½ and suppose in this case that r>r_(up). Then, for 0≦x≦x* the difference g(x)−x is greater than or equal to

${gap} = {\frac{r - r_{up}}{{snr}\left( {\tau^{2} + r_{1}} \right)}.}$

Moreover, g(x)−x is at least (1−xν)GAP where

${GAP} = {\frac{r - r_{up}}{v\left( {\tau^{2} + r} \right)}.}$

Demonstration of Corollary 20:

With δ_(c)=0 the choice ζ=0 corresponds to r₁=0. At this ζ, the main part of r_(up) equals 2τ/√{square root over (2π)} since φ(0)=1/√{square root over (2π)} and the remainder rem equals ½ since Φ(0)=½. This produces the indicated value of r_(up). The monotonicity of g(x)−x in the δ_(c)=0 case yields, for x≦x*, a value at least as large as at x* where it is bounded by Lemma 18. This yields the first claim.

Next use the representation of g(x)−x as (1−xν)A(z_(x))/[ν(τ²+r)], where with δ_(c)=0 the A(z) is

A(z)=r−1−2τφ(z)−zφ(z)+[τ²+1−(τ+z)²]Φ(z).

It has derivative which simplifies to

A′(z)=−2(τ+z)Φ(z),

which is negative for z>−τ which includes the interval [z₀, z₁]. Accordingly A(z_(x)) is decreasing and its minimum for x in [0, x*] occurs at x*. Appealing to Lemma 18 completes the demonstration of Lemma 20. An alert to the reader: The above result together with the reliability bounds provides a demonstration that g_(L)(x) is such that a rate

/(1−r/τ²) is achieved with a moderately small fraction of mistakes, with high reliability. Here r/τ7 ² at least r_(up)/τ² is nearly equal to a constant times 1/τ, which is near 1/√{square root over (π log B)}. This is what can be achieved in the comparatively straightforward fashion of the first half of the manuscript.

Nevertheless, it would be better to have a bound with r_(up) of smaller order so that for large B the rate is closer to capacity. For that reason, next take advantage of the modification to the power allocation in which it is slightly leveled using a small positive δ_(c). When

is not large, these modifications make an improved rate target

_(B) which is below capacity by an expression of order 1/log B and likewise the fraction of mistakes target (corrected by the outer code) is improved to be an expressions of order 1/log B. This is certainly an improvement over 1/√{square root over (log B)}, and as already said herein, the reliability and rate tradeoff is not far from optimal in this regime. That is, if one wants rate substantially closer to capacity it would necessitate worse reliability. Moreover, for any fixed R<

it is certainly the case that with sufficient size B the rate R is less than

_(B), so that the existing results take effect.

Nevertheless, both of the 1/√{square root over (log B)} and 1/log B expressions are not impressively small.

This has compelled the authors to push in what follows in the rest of this manuscript to squeeze out as much as one can concerning the constant factor or other lower order factors. Even factors of 2 or 4 very much matter when one only has a log B. There are myriad aspects of the problem (via freedoms of the specified design) in which efforts are made push down the constants, and it persists as an active effort of the authors. Accordingly, it is anticipated that the inventors herein will provide further refinements of the material herein in the months to come.

Nevertheless, the comparatively simple results in the δ_(c)=0 case above and the bounds from δ_(c)>0 in what follows both constitute first proofs of performance achievement by practical schemes, that scale suitably in rate, reliability and complexity.

It is anticipated that some refinements will derive from the tools provided here by making slightly different specializations of the design parameters already introduced, to which the invention has associated rights to determine the implications of these specializations.

It is the presence of a practical high-performance encoder and decoder as well as the general tools of performance evaluation and of rate characterizations, e.g. via the update function g_(L)(x), that are the featured aspects of the invention to this point in the manuscript, and not the current value of the moderate constants that are develop in the pages to come.

The specific tools for refinement become detailed mathematical efforts that go beyond what most readers would want to digest. Nevertheless, proceed forward with inclusion of these since it does lead to specific instantiations of code parameter settings for which the drop from capacity is improved in its characteristics to be a specific multiple of 1/log B.

Monotonicity or unimodality of g(x)−x or of g_(low)(x)−x is used in the above gap characterizations for the δ_(c)=0 and δ_(c)=snr cases. It what follows, analogous shape properties are presented that include the intermediate case.

9.5 Showing g (x)>x in the Case of Some Leveling:

Now allow small leveling of the power allocation, via choice of a small δ_(c)>0 and explore determination of lower bounds on the gap.

Use the inequality g(x)≧g_(low)(x)/(1+D(δ_(c))/snr) so that

${{g(x)} - x} \geq {\frac{{g_{low}(x)} - x - {x\; {{D\left( \delta_{c} \right)}/{snr}}}}{1 + {{D\left( \delta_{c} \right)}/{snr}}}.}$

This gap lower bound is expressible in terms of z=z_(x) using the results of Lemma 14 and the expression for x given immediately thereafter. Indeed,

${{g_{low}(x)} - x} = {\frac{u_{x}R}{v\; C^{\prime}}\frac{A\left( z_{x} \right)}{\tau^{2}}}$

where for R=

/(1+r/τ²) the function A(z) simplifies to

r−1−2τφ(z)−zφ(z)+[τ²+1−(1−Δ_(c))(τ+z)²]Φ(z),

where Δ_(c)=log(1+δ_(c)). The multiplier u_(x)R/(ν

) is also (1+δ_(c))/(snr(1+z/τ)²). From the expression for x in terms of z write

$x = {1 - \frac{\delta_{c}}{snr} + {\frac{\left( {1 + \delta_{c}} \right)}{{{snr}\left( {1 + {z/\tau}} \right)}^{2}}{\left( {\left( {1 + {z/\tau}} \right)^{2} - 1 - {r/\tau^{2}}} \right).}}}$

Accordingly,

g _(low)(x)−x−xD(δ_(c))/snr=G(z _(x))

where G(z) is the function

${G(z)} = {{\frac{1 + \delta_{c}}{\left( {\tau + z} \right)^{2}}\frac{\overset{\sim}{A}(z)}{snr}} - {\frac{D\left( \delta_{c} \right)}{snr}\left( {1 - {\delta_{c}/{snr}}} \right)}}$ with ${\overset{\sim}{A}(z)} = {{A(z)} + {\frac{\tau^{2}{D\left( \delta_{c} \right)}}{snr}{\left( {1 - \left( {1 + {z/\tau}} \right)^{2} + {r/\tau^{2}}} \right).}}}$

In this way the gap lower bound is expressed through the function G(z) evaluated at z=z_(x). Regions for x in [0,1] where g_(low)(x)−x−xD(δ_(c))/snr is decreasing or increasing, have corresponding regions of decrease or increase of G(z) in [z₀, z₁]. The following lemma characterizes the shape of the lower bound on the gap.

Definition:

A continuous function G(z) is said to be unimodal in an interval if there is a value z_(max) such that G(z) is increasing for any values to the left of z_(max) and decreasing for any values to the right of z_(max). This includes the case of decreasing or increasing functions with z_(max) at the left or right end point of the interval, respectively.

Likewise, with domain starting at z₀, a continuous function G(z) is said to have at most one oscillation if there is a value z_(G)≧z₀ such that G(z) is decreasing for any values of z between z₀ and z_(G), and unimodal to the right of z_(G). Call the point z_(G) the critical value of G.

Functions with at most one oscillation in an interval [z₀, z*] have the useful conclusion that the minimum over the interval is determined by the minimum of the values at z_(G) and z*.

Lemma 21.

Shape properties of the gap. Suppose the rate satisfies

R≦C′(1+D(δ_(c))/snr)/(1+1/τ²).

The function g_(low)(x)−x−xD(δ_(c))/snr has at most one oscillation in [0, 1]. Likewise, the functions A(z) and G(z) have at most one oscillation for z≧−τ and their critical values are denoted z_(A) and z_(G). For all Δ_(c)≧0, these satisfy z_(A)≦z_(G) and z_(A)≦−τ/2+1, which is less than or equal to 0 if τ≧2.

Moreover, if either Δ_(c)≦⅔ or Δ_(c)≧2√{square root over (2π)}half/τ, then z_(G) is also less than or equal to 0. Here half is an expression not much more than ½ as given in the proof.

The proof of Lemma 21 is in the appendix.

Note that τ≧3√{square root over (2π)} half is sufficient to ensure that one or the other of the two conditions on A must hold. That would entail a value of B more than e^(2.25π)>1174. Such size of B is reasonable, though not essential as one may choose directly to have a small value of Δ_(c) not more than ⅔.

One can pin down the location of z_(c); further, under additional conditions on Δ_(c). However, precise knowledge of the value of z_(G) is not essential because the shape properties allow us to take advantage of tight lower bounds on A(z) for negative z as discussed in the next lemma.

It holds that z_(A)≦τ/2+1 and under conditions on Δ_(c) that z_(A)≦−τ/2. For −τ/2+1 to be negative, it is assumed that τ≧2, as is the case when B≧e². Preferably B is much larger.

Lemma 22

Lower bounding A(z) for negative z: In an initial interval [−τ,t] with t=−τ/2 or −τ/2+1, the function A(z) is lower bounded by

A(z)≧r−1−ε,

where ε is (2τ+t)/t²)φ(t). In particular for t=−τ/2 it is (6/τ)φ(τ/2), not more than (3/√{square root over (π log B)})(1/B)^(0.5), polynomially small in 1/B. Likewise, if t=−τ/2+1, the ε remains polynomially

If Δ_(c)≧4/√{square root over (2π)}−1/τ, then the above inequality holds for all negative z.

${\min\limits_{{- \tau} \leq z \leq 0}{A(z)}} \geq {r - 1 - \varepsilon}$ with  ε = (6/τ)φ(τ/2).

Finally if also Δ_(c)≧8/τ² then for z between −τ/2 and 0, the

Δ(z)>r−1

which is strictly greater than r−1 with no need for ε.

Demonstration of Lemma 22:

First, examine A(z) for z in an initial interval of the form [−τ,t]. For such negative z one has that A(z) is at least r−1−2τφ(z) which is at least r−1−2τφ(t). This is seen by observing that in the expression for A(z), the −zφ(z) term and the term involving Φ(z) are positive for z≦0. So for ε one can use 2τφ(t).

Further analysis of A(z) permits the improved value of e as stated in the lemma. Indeed, A(z) may be expressed as

A ₀(z)=r−1−(2τ+z)φ(z)−(2τ+z)zΦ(z)+Φ(z)

plus an additional amount [Δ_(c)(τ+z)²]Φ(z) which is positive. It's derivative simplifies as in the analysis in the previous lemma and it is less than or equal to 0 for −τ≦z≦0, so A₀(z) is a decreasing function of z, so its minimum in [−τ,t] occurs at z=t.

Recall that along with the upper bound |z|Φ(z)≦φ(z), there is the lower bound of Feller, |z|Φ(z)>[1−1/z²]φ(z), or the improvement in the appendix which yields |z|Φ(z)≧[1−1/(z²+1)]φ(z), which is Φ(z)≧(|z|/(z²+1)φ(z), for negative z. Accordingly obtain

A ₀(z)≧r−1−[(2τ+z)/(z ²+1)]φ(z).

At z=t=−τ/2 the amount by which it is less than r−1 is [(3/2)τ/(τ²/4+1)]φ(τ/2) not more than (6/τ)φ(τ/2), which is not more than (6/√{square root over (2π2 log B)})(1/B)^(1/2). An analogous bound holds at t=−τ/2+1.

Next consider the value of A(z) at z=0. Recall that A(z) equals

r−1−(2τ+z)φ(z)+[τ²+1−(1−Δ_(c))(τ+z)²]Φ(z).

At z=0 it is

r−1−2τ/√{square root over (2π)}+[1+Δ_(c)τ²]/2

which is at least r−1 if Δ_(C)τ²≧4τ/√{square root over (2π)}−1, that is, if Δ_(c)≧4/(τ√{square root over (2π)})−1/τ². This is seen to be greater than Δ_(c)**=2/(τ²/4+2), having assumed that τ at least 2. So by the previous lemma A(z) is unimodal to the right of t=τ/2, and it follows that the bound r−1−ε holds for all z in [−τ, 0].

Finally, for A(z) in the form

r−1−(2τ+z)φ(z)−(2τ+z)zΦ(z)+[1+Δ_(c)(τ+z)²]Φ(z),

replace −zΦ(z), which is |z|Φ(z) with its lower bound φ(z)−(1/|z|)Φ(z) for negative z from the same inequality in the appendix. Then the terms involving φ(z) cancel and the lower bound on A(z) becomes

r−1+[1+Δ_(c)(τ+z)²−(2τ+z)/|z|]Φ(z)

which is

r−1+[Δ_(c)(τ+z)²+2(τ+z)/z]Φ(z).

In particular at z=−τ/2 it is r−1+[Δ_(c)τ²/4−2]Φ(−τ/2) which exceeds r−1 by a positive amount due to die stated conditions on Δ_(c). To determine die region in which the expression in brackets is positive more precisely, proceed as follows. Factoring out τ+z the expression remaining in the brackets is

Δ_(c)(τ+z)+2/z.

It starts out negative just to the right of −τ and it hits 0 for z solving the quadratic Δ_(c)(τ+z)z+2=0, for which the left and right roots are z=[−τ±√{square root over (τ²−8/Δ_(c))}]/2, again centered at −τ/2. The left root is near −τ[1−2/(Δ_(c)τ)]. So at least between these roots, and in particular between the left root and the point −τ/2, the A(z)≧r−1. The existence of these roots is implied by Δ_(c)>8/τ² which in turn is greater than Δ_(c)**=8/(τ²+8). So by the analysis of the previous Lemma, A′(z) is positive at −τ/2 and A(z) is unimodal to the right of −τ/2. Consequently A(z) remains at least r−1 for all z between the left root and 0. This completes the demonstration of Lemma 22.

Exact evaluation of G(z_(crit)) is problematic, so instead take advantage for negative z of the tight lower bounds on G(z) that follow immediately from the above lower bounds on A(z). With no conditions on Δ_(c), use A(z)≧r−1−ε for z≦−τ/2+1 and unimodality of A(z) to the right of there, to allow us to combine this with the bounds at z*. This use of unimodality of A(z) has the slight disadvantage of needing to replace u_(x)=1−xν with the lower bound 1−x*ν, and needing to replace −xD(δ_(c))/snr with −x*D(δ_(c))/snr, to obtain the combined lower bound on G(z) via A(z). In contrast, with conditions on Δ_(c), use directly that the minimum of G(z) occurs at the minimum of the values at a negative z_(G) and at z*, allowing slight improvement on the gap.

Lemma 23.

Lower bounding G(z) for negative z: If Δ_(c)τ≧4/√{square root over (2π)}−1/2τ, then for −τ<z≦0, setting z′=z(1+z/2τ), the function G(z) is at least

${{\left( {1 + \delta_{c}} \right)\frac{r - 1 - \varepsilon - {\left( {{2z^{\prime}\tau} - r} \right){{D\left( \delta_{c} \right)}/{snr}}}}{\left( {\tau + z} \right)^{2}{snr}}} - {\frac{D\left( \delta_{c} \right)}{snr}\left( {1 - \frac{\delta_{c}}{snr}} \right)}},$

which foe r≧(1+ε)/(1+D(δ_(c))/snr), yields G(z) at least

$\frac{r - 1 - \varepsilon + {r\; {D\left( \delta_{c} \right)}{snr}}}{\tau^{2}{snr}} - {\frac{D\left( \delta_{c} \right)}{snr}{\left( {1 - \frac{\delta_{c}}{snr}} \right).}}$

Consequently, the gap g(x)−x for z_(x)≦0 is at least

$\frac{r - r_{down}}{{snr}\; {\tau^{2}/\left( {1 + \delta_{c}} \right)}}$ with ${r_{down} = \frac{1 + \varepsilon + {\tau^{2}{D\left( \delta_{c} \right)}{\left( {1 - {\delta_{c}/{snr}}} \right)/\left( {1 + \delta_{c}} \right)}}}{1 + {{D\left( \delta_{c} \right)}/{snr}}}},$

less than 1+ε+τ²D(δ_(c)). If also Δ_(c)≧8/τ², then the above inequalities hold for −τ/2≦z≦0 without the ε.

Demonstration of Lemma 23

Using the relationship between G(z) and A(z) given prior to Lemma 21, these conclusions follow immediately from plugging in the bounds on A(z) from Lemma 22.

Next combine the gap bounds for negative z with the gap bound for z*. This allows to show that g(x)−x has a positive gap as long as the rate drop from capacity is such that r>τ_(crit) for a value of r_(crit) as identified. This holds for a range of choices of r₁ including 0.

Lemma 24.

The minimum value of the gap. For 0≦x≦x*, of r>r_(crit), then the g(x)−x is at least

$\frac{r - r_{crit}}{{snr}\left( {\tau^{2} + r_{1}} \right)}.$

This holds for an r_(crit) not more than r_(crit)* given by

max{(τ² +r ₁)D(δ_(c))+1+ε,r ₁+(2τ+ζ)φ(ζ)+rem},

where, as before, rem=[(τ²+r₁)D(δ_(c))+1−r₁ ]Φ(ζ) and ε is as given in Lemma

with t=−τ/2+1. Then g_(L)(x)−x on [0, x*] has gap at least

${gap} = {\frac{r - r_{crit}}{{snr}\left( {\tau^{2} + r_{1}} \right)} - {\frac{2C}{vL}.}}$

Consequently, any specified positive value of gap is achieved by setting

r=r _(crit) +snr(τ² +r ₁)[gap+2

/(Lν)].

The contribution to the denominator of the rate expression (1+D(δ_(c))/snr)(1+r_(crit)/τ²) at r_(crit) has the representation in terms of r_(crit)* as

1+(1+r ₁/τ²)D(δ_(c))/snr+r _(crit)*/τ².

If Δ_(c)≧4/(τ√{square root over (2π)})−1/τ² and either Δ_(c)≦2/3 or Δ_(c)≧√{square root over (2π)}half/τ, then in the above characterization of r_(crit)* the D(δ_(c)) in the first expression of the max may be reduced to D(δ_(c))(1−δ_(c)/snr).

Moreover, there is the refinement that g(x)−x is at least

${\frac{1}{snr}\min \left\{ {\frac{r - r_{down}}{\tau^{2}/\left( {1 + \delta_{c}} \right)},\frac{r - r_{up}}{\tau^{2} + r_{1}}} \right\}},$

where r_(down) and r_(up) are as given in Lemmas

and 18, respectively. If also δ_(x) is such that the z_(G) Of order −√{square root over (2 log(τ/δ_(c)))} is between −τ/2 and 0, then the ε above may be omitted.

For given ζ>0, adjust r₁ to optimize the value of r_(crit)* in the next subsection.

The proof of the lemma will improve on the statement of the lemma by exhibiting an improved value of r_(crit) that makes use of r_(crit)*.

Demonstration of Lemma 24:

Replacing g(x) by its lower bound g_(low)(x)/[1+D(δ_(c))/snr] the g(x)−x is at least

${{gap}_{low}(x)} = \frac{{g_{low}(x)} - {x\left\lbrack {1 + {{D\left( \delta_{c} \right)}/{snr}}} \right\rbrack}}{1 + {{D\left( \delta_{c} \right)}/{snr}}}$

which is

$\frac{{\left( {u_{x}/v} \right)\left( {R/C^{\prime}} \right){{A\left( z_{x} \right)}/\tau^{2}}} - {x\; {{D\left( \delta_{c} \right)}/{snr}}}}{1 + {{D\left( \delta_{c} \right)}/{snr}}}.$

For 0≦x≦x* the u_(x)R/(ν

) is at least its value at x* which is 1/[snr(1+r₁/τ²)], so gap_(low)(x) is at least

$\frac{{{A\left( z_{x} \right)}/\left\lbrack {{snr}\left( {\tau^{2} + r_{1}} \right)} \right\rbrack} - {x^{*}{{D\left( \delta_{c} \right)}/{snr}}}}{1 + {{D\left( \delta_{c} \right)}/{snr}}},$

which may also be written

$\frac{{A\left( z_{x} \right)} - {\left( {\tau^{2} + r_{1}} \right)x^{*}{D\left( \delta_{c} \right)}}}{{{snr}\left( {\tau^{2} + r_{1}} \right)}\left\lbrack {1 + {{D\left( \delta_{c} \right)}/{snr}}} \right\rbrack},$

which by Lemma 18 coincides with (r−r_(up))/[snr(τ²+r₁)] at x=x*.

Now recall from Lemma 21 that A(z) is unimodal for z≧t, where t is −τ/2 or −τ/2+1, depending on the value of Δ_(c). As seen, when Δ_(c) is small, the A(z) is in fact decreasing and so one may use r_(crit)=r_(up) from the gap at x*. For other Δ_(c), the unimodality of A(z) for z≧t implies that the minimum of A(z) over [−τ,z*] is equal to that of over [−τ,t]∪{z*}. As seen in Lemma 22, the minimum of A(z) in [−τ,t] is given by A_(low)=r−1−ε. Consequently, the g(x)−x on 0≦x≦x* is at least

$\min {\left\{ {\frac{r - 1 - \varepsilon - {\left( {\tau^{2} + r_{1}} \right)x^{*}{D\left( \delta_{c} \right)}}}{{{snr}\left( {\tau^{2} + r_{1}} \right)}\left( {1 + {{D\left( \delta_{c} \right)}/{snr}}} \right)},\frac{r - r_{up}}{{snr}\left( {\tau^{2} + r_{1}} \right)}} \right\}.}$

Now x*=1−(r−r₁)/[snr(τ²+r₁)]. So (τ²+r₁)x* is equal to (τ²+r₁)−(r−r₁)/snr. Then, gathering the terms involving r, note that a factor of 1+D(δ_(c))/snr arises that cancels the corresponding factor from the denominator for the part involving T. Extract the value r shared by the two terms in the minimum to obtain that the above expression is at least

$\frac{r - r_{crit}}{{snr}\left( {\tau^{2} + r_{1}} \right)}$

where here r_(crit) is given by

$\max {\left\{ {\frac{{\left( {\tau^{2} + r_{1}} \right){D\left( \delta_{c} \right)}} + 1 + \varepsilon + {r_{1}{{D\left( \delta_{c} \right)}/{snr}}}}{1 + {{D\left( \delta_{c} \right)}/{snr}}},r_{up}} \right\}.}$

Arrange 1+D(δ_(c))/snr as a common denominator. From the definition of r_(up) its numerator becomes r₁[1+D(δ_(c))/snr](2τ+ζ)φ(z)+rem. It follows that in the numerator the two expressions in the max share the term r₁D(δ_(c))/snr. Accordingly, with α=[D(δ_(c))/snr]/[1+D(δ_(c))/snr] and 1−α=1/[1+D(δ_(c))/snr], it holds that

r _(crit) =αr ₁+(1−α)r _(crit)*,

with r_(crit)* given by

max{(τ² +r ₁)D(δ_(c))+1+ε,r ₁+(2τ+ζ)φ(ζ)+rem}.

This r_(crit)* exceeds r₁, because the amount added to r₁ in the second expression in the max is the same as the numerator of the shortfall δ* which is positive. Hence αr₁+(1−α)r_(crit)* is less than r_(crit)*. So the r_(crit) here improves somewhat on the choice in the statement of the Lemma.

Moreover, from

$r_{crit} = \frac{r_{crit}^{*} + {r_{1}{{D\left( \delta_{c} \right)}/{snr}}}}{1 + {{D\left( \delta_{c} \right)}/{snr}}}$

it follows that (1+D(δ_(c))/snr)(1+r_(crit)/τ²) is equal to

${1 + \frac{\left( {1 + {r_{1}/\tau^{2}}} \right){D\left( \delta_{c} \right)}}{snr} + \frac{r_{crit}^{*}}{\tau^{2}}},$

as claimed.

Finally, for the last conclusion of the Lemma, it follows from the fact that

${{\min\limits_{{- \tau} < z \leq z^{*}}{G(z)}} = {\min \left\{ {{G\left( z_{G} \right)},{G\left( z^{*} \right)}} \right\}}},$

invoking z_(G)≦0 and combining the bounds form Lemmas 23 and 18. This completes the demonstration of Lemma 24.

Note from the form of rem and using 1−Φ(ζ)=Φ(ζ) that r_(crit)* may be written

max{(τ² +r ₁)D(δ_(x))+ε,(r ₁−1)Φ(ζ)+(2τ+ζ)φ(z)+(τ2r ₁)D(δ_(c)) Φ(ζ)}.

Thus [(τ²+r₁)D(δ_(c))+1] appears both in the first expression and as a multiplier of Φ(ζ) in the remainder of the second expression in the max.

To clean the upcoming expressions, note that upon replacing the second expression in this max with the bound in which the polynomially small e is added to it, then r_(crit)*−1−ε becomes independent of ε. Accordingly, henceforth herein make that redefinition of r_(crit)*. Denoting {tilde over (r)}_(crit)*=r_(crit)*−1−ε it becomes

max{(τ² −r ₁)D(δ_(c)),(r ₁−1)Φ(ζ)+(2τ+ζ)φ(z)+(τ² +r ₁)D(δ_(c)) Φ(ζ)}.

Evaluation of the best r_(crit)* arises in the next subsection from determination of the r₁ that minimizes it.

9.6 Determination of δ_(c): Here suitable choices of the leveling parameter δ_(c) are determined. Recall, δ_(c)=0 corresponds to no-leveling and δ_(c)=snr corresponds to the constant power allocation, and both will have their role for very large and very small snr, respectively. Values in between are helpful in conjunction with controlling the rate drop parameter r_(crit).

Recall the relationship 1+δ_(c)=(1+ζ/τ)²/(1+r₁/τ²), used in analysis of the gap based on g_(low)(x), where ζ is the value of z_(x) at the upper end point x* of the interval in which the gap property is invoked. In this subsection, hold ζ fixed and ask for the determination of a suitable choice of δ_(c).

In view of the indicated relationship this is equivalent to the determination a choice of r₁. There are choices that arise in obtaining manageable bounds on the rate drop. One is to set r₁=0 at which δ_(c) is near 2ζ/τ, proceeding with a case analysis depending on which of the two terms of r_(crit)* is largest. In the end this choice permits roughly the right form of bounds, but noticeable improvements in the constants arise with suitable non-zero r₁ in certain regimes.

Secondly, as determined in this section, one can find the r₁ or equivalently δ_(c)=δ_(match) at which the two expressions in the definition of r_(crit)* match. In some cases this provides the minimum value of r_(crit)*.

Thirdly, keep in mind that a small mistake rate δ_(mis)* is desired as well as a small drop from capacity of the inner code. The use of the overall rate of the composite code provides means to express a combination of δ_(mis)*, r_(crit)* and D(δ_(c))/snr to optimize.

In this subsection the optimization of δ_(c) for each ζ is addressed, and then in the next subsection the choice of nearly best values of ζ. In particular, this analysis provides means to determine regimes for which it is best overall to use δ_(match) or for which it is best to use instead δ_(c)=0 or δ_(c)=snr.

For ζ>−τ, define ζ′ by

ζ′=ζ(1+ζ/2τ)

for which (1+ζ/τ)²=1+2ζ′/τ and define ψ=ψ(ζ) by

ψ=(2τ+ζ)φ(ζ)/Φ(ζ)

and γ=γ(ζ) by the small value

γ=2ζ′/τ+(ψ−1)/τ².

Lemma 25.

Match making. Given ζ, the choice of δ_(c)=δ_(match) that makes the two expressions in the definition of r_(crit)* be equal is given by

1+δ_(c) =e ^(γ/(1+ζ/τ)) ² .

at which

1+r ₁/τ²=(1+ζ/τ)² e ^(−γ/(1+ζ/τ)) ².

This δ_(c) is non-negative for ζ such that γ≧0. At this δ_(c)=δ_(match) the value of {tilde over (r)}^(crit)*=r_(crit)*−1−ε is equal to

τ²)(1+r ₁/τ²)D=(δ_(c))=r ₁+ψ−1,

which yields {tilde over (r)}_(crit)/τ² equal to

(1+ζ/τ)² [e ^(−γ/(1+ζ/τ)) ² −1]+γ,

which is less than γ²/[2(1+ζ/τ)²] for γ>0. Moreover, the contribution δ_(mis)* to the mistake rate as in Lemma 17, at this choice of δ_(c) and corresponding r₁, is equal to

$\delta_{mis}^{*} = {\frac{\psi}{2\left( {\tau + \zeta} \right)^{2}C}.}$

Remark:

Note from the definition of ψ and ζ′ that

$\gamma = {\frac{{\left( {2 + {\zeta/\tau}} \right)\left( {\zeta + {{\varphi (\zeta)}/{\Phi (\zeta)}}} \right)} - {1/\tau}}{\tau}.}$

Thus γ is near 2(ζ+φ(ζ)/Φ(ζ))/τ.

Using the tail properties of the normal given in the appendix, the expression ζ+φ(ζ)/Φ(ζ) is seen to be non-negative and increasing in ζ for all ζ on the line, near to 1/|ζ| for sufficiently negative ζ, and at least ζ for all positive ζ, In particular, γ is found to be non-negative for ζ at least slightly to the right of −τ.

Meanwhile, by such tail properties, φ(ζ)/Φ(ζ) is near |ζ| for sufficiently negative ζ, so to keep δ_(mis)* small it is desired to avoid such ζ unless the capacity

is very large. At ζ=0 the ψ equals 4π/√{square root over (2π)} so the δ_(mis)* there is of order 1/τ. When

is not large, a somewhat positive ζ is preferred to produce a small δ_(mis)* in balance with the rate drop contribution r_(crit)*/τ².

The ζ=0 case is illustrative for the behavior of γ and related quantities. There γ is (ψ−1)/τ² equal to 4/τ√{square root over (2π)}−1/τ², with which δ_(c)=e^(γ)−1. Also r₁/τ²=e^(γ)−1. The {tilde over (r)}_(crit)*/τ²=r₁/τ²+(ψ−1)/τ² is then equal e^(−γ)−1+γ near to and upper bounded by γ²/2=(½)(ω−1)²/τ⁴ less than 4/τ²π. In this ζ=0 case, the slightly positive δ_(c), with associated negative r₁, is sufficient to cancel the (ψ−1)/τ² part of {tilde over (r)}_(crit)*/τ², leaving just the small amount bounded by (½)(ψ−1)²/τ⁴. With (ψ−1)/τ² less than 2, that is a strictly superior value for {tilde over (r)}_(crit)*/τ² than obtained with δ_(c)=0 and ζ=0 for which {tilde over (r)}_(crit)*/τ² is (ψ−1)/τ².

Demonstration of Lemma 25:

The r_(crit)*−ε is the maximum of the two expressions

(τ² +r ₁)D(δ_(c))+1

and

r ₁Φ(ζ)+(2τ+ζ)φ(z)+[(τ² +r ₁)D(δ_(c))+1] Φ(ζ).

Equating these, grouping like terms together using 1− Φ(ζ)=Φ(ζ) and then dividing through by Φ(ζ) yields

(τ² +r ₁)D(δ_(c))−r ₁=ψ−1.

Using τ²+r₁ equal to (τ+ζ)²/(1+δ_(c)) and [D(δ_(c))−1]/(1+δ_(c)) equal to log(1+δ_(c))−1 the above equation may be written

(τ+ζ)²[log(1+δ_(c))−1]+τ²=ψ−1.

Rearranging, it is

${{\log \left( {1 + \delta_{c}} \right)} = {1 + \frac{\tau^{2} + \left( {\psi - 1} \right)}{\left( {\tau + \zeta} \right)^{2}}}},$

where the right side may also be written γ/(1+ζ/τ)². Exponentiating establishes the solution for 1+δ_(c), with corresponding r₁ as indicated. Let's call the value that produces this equality δ_(c)=δ_(match). At this solution the value of {tilde over (r)}_(crit)*=r_(crit)*−1−ε satisfies

(τ² +r ₁)D(ε_(c))=r ₁+ψ−1.

Likewise, from the identity (τ²+r₁)D(δ_(c))−(r₁−1)=ψ, multiplying by Φ(ζ) this establishes that the remainder used in Lemma 16 is in the present case equal to rem=ψ Φ(ζ), while the main part (2τ+ζ)φ(ζ) is equal to ψΦ(ζ). Adding them using Φ(ζ)+ Φ(ζ)=1 shows that (2τ+ζ)φ(ζ)+rem is equal ψ. This is the numerator in the mistake rate expression δ_(mis)*.

Using the form of r₁ the above expression for {tilde over (r)}_(crit)*/τ² may also be written

(1+ζ/τ)² [e ^(−γ/(1+ζ/τ)) ² −1]+γ.

With y=γ/(1+ζ/τ)² positive, the expression in the brackets is e^(−y)−1 which is less than y+y²/2. Plugging that in, the part linear in y cancels, leaving the claimed bound γ²/[2/(1+ζ/τ)²]. This completes the demonstration of Lemma 25.

Lemma 26.

The optimum δ_(c) quartet. For each ζ, consider the following minimizations. First, consider the minimization of r_(crit)* for δ_(c) in the interval [0, snr]. Its minimum occurs at the positive δ_(c) which is the minimum of the three values δ_(thresh) ₀ , δ_(match), and snr, where δ_(thresh) ₀ =Φ(ζ)/ Φ(ζ).

Second, consider the minimization of

(1+D(δ_(c))/snr)(1+r _(crit)/τ²)

as arises in the denominator of the detailed rate expression. Its minimum for δ_(c) in [0, snr) occurs at the positive δ_(c) which is the minimum of the two values δ_(thresh) ₁ and δ_(match), where δ_(thresh) ₁ is Φ(ζ)/[ Φ(ζ)+1/snr].

Third, consider the minimization of the following combination of contributions to the inner code rate drop and the simplified mistake rate,

δ_(mis,simp)*+(1+D(δ_(c))/snr)(1+r _(crit)/τ²)−1,

for δ_(c) in [0, snr). For Φ(ζ)≦1/(1+2

) its minimum occurs at δ_(c)=0, otherwise it occurs at the positive δ_(c) which is the minimum of the two values δ_(thresh) and δ_(match) where

$\delta_{thresh} = {\frac{{\Phi (\zeta)} - {{{\overset{\_}{\Phi}(\zeta)}/2}C}}{{1/{snr}} + {{\overset{\_}{\Phi}(\zeta)}\left( {1 + {{1/2}C}} \right)}}.}$

The same conclusion holds icing δ_(mis)*=δ_(mis,simp)*/(1+ζ/τ)², replacing the occurrences of 2

in the previous sentence with 2

(1+ζ/τ)². Finally, set

Δ_(ζ,δ) _(c) =δ_(mis)*+(1+D(δ_(c))/snr)(1+r _(crit)/τ²)−1

and extend the minimization to [0, snr] using the previously given specialized values in the δ_(c)=snr case. Then for each ζ the minimum Δ_(ζ,δ) _(c) for δ_(c) in [0, snr] is equal to the minimum over the four values 0, δ_(thresh), δ_(match) and snr.

Remark:

The Δ_(ζ,δ) _(c) , when optimized also over ζ, will provide the Δ_(shape) summarized in the introduction. As shown in the next section, motivation for it arises from the total drop rate from capacity of the composition of the sparse superposition code with the outer Reed-Solomon code. For now just think of it as desirable to choose parameters that achieve a good combination of low rate drop and low fraction of section mistakes. As the proof here shows, the proposed combination is convenient for the calculus of this optimization.

Recall for 0≦δ_(c)<snr that (1+D(δ_(c))/snr)(1+r_(crit)/τ²) equals (1+r₁/τ²)D(δ_(c))/snr+1+r_(crit)*/τ². In contrast, for δ_(c)=snr, set δ_(mis)*= Φ(ζ) and r_(crit)=max{r_(up),0}, using the form of r_(up) previously given for this case. These different forms arise because the g_(low) (x) bounds are used for 0≦δ_(c)<snr, whereas g(x) is used directly for δ_(c)=snr.

Demonstration of Lemma 26:

To determine the δ_(c) minimizing r_(crit)*, in the definition of r_(crit)*−1−ε write the first expression (τ²+r₁)D(δ_(c)) in terms of δ_(c) as

$\left( {\tau + \zeta} \right)^{2}{\frac{D\left( \delta_{c} \right)}{\left( {1 + \delta_{c}} \right)}.}$

Take its derivative with respect to δ_(c). The ratio D(δ_(c))/(1+δ_(c)) has derivative that is equal to [D′(δ_(c))(1+δ_(c))−D(δ_(c))] divided by (1+δ_(c))². Now from the form of D(δ_(c)), its derivative D′(δ_(c)) is log(1+δ_(c)), so the expression in brackets simplifies to δ_(c), which is nou-negative, and multiplying by the positive factor (τ+ζ)²/(1+δ_(c))² provides the desired derivative. Thus this first expression is increasing in δ_(c), strictly so for δ_(c)>0. As for the second expression in the maximum, it is equal to the first expression times Φ(ζ) plus r₁+ψ−1 times Φ(ζ). So from the relationship of r₁ and δ_(c), its derivative is equal to [δ_(c) Φ(ζ)−Φ(ζ)] times the same (τ+ζ)²/(1+δ_(c))². So the value of the derivative of the first expression is larger than that of the second expression, and accordingly the maximum of the two expressions equals the first expression for δ_(c)≧δ_(match) and equals the second expression for δ_(c)<δ_(match). The derivative of the second expression, being the multiple of [δ_(c) Φ(ζ)−Φ(ζ)] is initially negative so that the expression is initialing decreasing, up to the point δ_(thresh) ₀ =Φ(ζ)/ Φ(ζ) at which the derivative of this second expression is 0, so the optimizer of r_(crit)* occurs at the smallest of the three values δ_(match), δ_(thresh), and the right end point snr of the interval of consideration.

To minimize (1+D(δ_(c))/snr)(1+r_(crit)/τ²)−1, multiplying through by τ² recall that it equals (τ²+r₁)D(δ_(c))/snr+r_(crit)* for 0≦δ_(c)<snr. Add to the previous derivative values the amount δ_(c)/snr, which is again multiplied by the same factor (τ+ζ)²/(1+δ_(c))₂. The first expression is still increasing. The second expression, after accounting for that factor, has derivative

δ_(c) /snr+δ _(c) Φ(ζ)−Φ(ζ).

It is still initially negative and hits 0 at δ_(thresh) ₁ =Φ(ζ)/[ Φ(ζ)+1/snr], which is again the minimizer if it occurs before δ_(match)Otherwise, if δ_(match) is smaller than δ_(thresh) _(.) then, since to the right of δ_(match) the maximum equals the increasing first expression, it follows that δ_(match) is the minimizer.

Next determine the minimizer of the criterion that combines the rate drop contribution with the simplified section mistake contribution δ_(mis,simp)*. Multiplying through by τ², added the quantity (τ²+r₁)D(δ_(c))−r₁+1 times Φ(ζ)/2

plus the amount (2τ+(ζ)φ(ζ)/2

not depending on δ_(c). So its derivative adds the expression (δ_(c)+1) Φ(ζ)/2

times the same the factor (τ+ζ)²/(1+δ_(c))². Thus, when the first part of the max is active, the derivative, after accounting for that factor, is

δ_(c)+δ_(c) /snr+(1+δ_(c)) Φ(ζ)/2

,

whereas, when the second part of the max is active it is

δ_(c) Φ(ζ)−Φ(ζ)+δ_(c) snr+(1+δ_(c)) Φ(ζ)/2

Again the first of these is positive and greater than the second. Where the value δ_(c) is relative to _(δ) _(match) determines which part of the max is active. For δ_(c)<δ_(match) it is the second. Initially, at δ_(c)=0, it is

−Φ(ζ)+ Φ(ζ)/2

,

which is (1/2

)[1−Φ(ζ)(1+2

)]. If ζ is small enough that Φ(ζ)≦1/(1+2

), this is at least 0. Then the criterion is increasing to the right of δ_(c)=0, whence δ_(c)=0 is the minimizer. Else if Φ(ξ)<1/(1+2

) then initially, the derivative is negative and the criterion is initially decreasing. Then as before the minimum value is either at δ_(thresh) or at δ_(match) whichever is smallest. Here δ_(thresh) is the point where the function based on the second expression in the maximum has 0 derivative. The same conclusions hold with δ_(mis)*=δ_(mis,simp)*/(1+ζ/τ²) in place of δ_(mis,simp) except that the denominator 2

is replaced with 2

(1+ζ/τ²). Examining δ_(thresh) ₁ and δ_(thresh), it is seen that these are less than snr. Nevertheless, when minimizing over [0, snr], the minimum can arise at snr because of the different form assigned to the expressions in that case. Accordingly the minimum of Δ_(ζ,δ) _(c) for δ_(c) [0, snr] is equal to the minimum over the four values 0, δ_(thresh), δ_(match) and snr, referred to as the optimum δ_(c) quartet. This completes the demonstration of Lemma 26.

Remark:

To be explicit as to the form of Δ_(ζ,δ) _(c) with δ_(c)=snr, recall that in this case 1+r_(up)/τ² is

(1−snr Φ(ζ))(1+ζ/τ)²/(1+snr).

Consequently Δ_(ζ,δ) _(c) =δ_(mis)*+(1+D(δ_(c))/snr)(1+r_(crit)/τ²)−1, in this δ_(c)=snr case, becomes

${{\overset{\_}{\Phi}(\zeta)} + {\frac{\left( {1 + {{D({snr})}/{snr}}} \right)}{\left( {1 + {snr}} \right)}\left( {1 + {{snr}{\overset{\_}{\Phi}(\zeta)}}} \right)\left( {1 + {\zeta/\tau}} \right)^{2}} - 1},$

when r_(up)≧0. For r_(up)<0 as is true for sufficiently small contributions from snr Φ(ζ) and ζ/τ, simply set r_(crit)=0 to avoid complications from the conditions of Corollary 19. Then Δ_(ζ,ξ) _(c) becomes

Φ(ζ)+D(snr)/snr.

9.7 Inequalities for ψ, γ, and {tilde over (r)}_(crit):

At δ_(c)=δ_(match), the r_(crit)* is examined further. Previously, in Lemma 25 the expression {tilde over (r)}_(crit)*/τ² is shown to be less than γ²/[2(1+ζ/τ)²]. Now this bound is refined in the cases of negative and positive ζ. For negative ζ it is shown that γ≦2/τ|ζ| and for positive |ζ| it is shown that {tilde over (r)}_(crit)* is not more than max{2(ζ′)², ψ−1}. For sufficiently positive ζ it is not more than 2ζ².

Recall that γ is less than

$\frac{\left( {2 + {\zeta/\tau}} \right)\left( {\zeta + {{\varphi (\zeta)}/{\Phi (\zeta)}}} \right)}{\tau}$

and that ψ=(2τ+ζ)φ(ζ)/Φ(ζ).

Lemma 27.

Inequalities for negative ζ. For −τ<ζ≦0, the γ is an increasing function less than min{2/|ζ|,4/√{square root over (2π)}}/τ. Likewise the function ψ is less than 2(|ζ|+1/|ζ|)τ.

Demonstration of Lemma 27:

For ζ≦0, the increasing factor 2 ζ/τ is less than 2 and the factor ζ+φ(ζ)/Φ(ζ) is non-negative, increasing, and less than 1/|ζ| by the normal tail inequalities in the appendix. At ζ=0 this factor is 2/√{square root over (2π)}. As for ψ the factor φ(ζ)/Φ(ζ) is at least |ζ| and not more than |ζ|+1/|ζ| for negative ζ again by the normal tail inequalities in the appendix (where improvements are given, especially for 0≦|ζ|≦1). This completes the demonstration of Lemma 27.

Now turn attention to non-negative ζ. Three bounds on {tilde over (r)}_(crit)* are given. The first based on γ²/2 and the other two more exacting to determine the relative effects of 2(ζ′)² and ψ−1.

Corollary 28.

For ζ≧0 it holds that {tilde over (r)}_(crit)*/τ²≦γ²/2 and

{tilde over (r)} _(crit)*≦2(ζ+φ(ζ)/Φ(ζ))².

Demonstration of Corollary 28:

By Lemma 25, the {tilde over (r)}_(crit)*/τ² is not more than γ²/[2(1+ζ/τ)²]. Now γ is not more than

$\frac{2\left( {1 + {{\zeta/2}\tau}} \right)\left( {\zeta + {{\varphi (\zeta)}/{\Phi (\zeta)}}} \right)}{\tau}$

Consequently, {tilde over (r)}_(crit)* is not more than

$\frac{2\left( {1 + {{\zeta/2}\tau}} \right)^{2}\left( {\zeta + {{\varphi (\zeta)}/{\Phi (\zeta)}}} \right)^{2}}{\left( {1 + {\zeta/\tau}} \right)^{2}}.$

Using 1+ζ/2τ not more than 1+ζ/τ, completes the demonstration of Lemma 28.

Lemma 29.

Direct r_(crit)* bounds. Let {tilde over (r)}_(crit)*=r_(crit)*−1−ε evaluated at δ_(match). Bounds are provided depending on whether D(2ζ′/τ) or (ψ−1)/τ² is larger. In the case D(2ζ′/τ)≧(ψ−1)/θ² the {tilde over (r)}_(crit)* satisfies

{tilde over (r)} _(crit)*/τ² ≦D(2ζ′/τ).

In any case, the value of {tilde over (r)}_(crit)*/τ² may be represented as an average of D(2ζ′/τ) and (ψ−1)/τ² plus small excess, where the weight assigned to (ψ−1)/τ² is proportional to the small 2ζ′/τ. Indeed {tilde over (r)}_(crit)*/τ² equals

$\frac{{D\left( {2{\zeta^{\prime}/\tau}} \right)} + {\frac{\left( {\psi - 1} \right)}{\tau^{2}}2{\zeta^{\prime}/\tau}}}{1 + {2{\zeta^{\prime}/\tau}}} + {excess}$

where excess is e^(−υ)−(1−υ) evaluated at

$\upsilon = {\frac{\frac{\psi - 1}{\tau^{2}} - {D\left( {2{\zeta^{\prime}/\tau}} \right)}}{1 + {2{\zeta^{\prime}/\tau}}}.}$

In the case (ψ−1)/τ²>D(2ζ′/τ) it satisfies

${excess} \leq {\frac{\left\lbrack {\frac{\psi - 1}{\tau^{2}} - {D\left( {2{\zeta^{\prime}/\tau}} \right)}} \right\rbrack^{2}}{2\left( {1 + {\zeta/\tau}} \right)^{4}}.}$

Demonstration of Lemma 29

With the relationship between τ₁ and δ_(c), recall (τ²+r₁)D(δ_(c)) is increasing in δ_(c) and hence decreasing in r₁. The r₁ that provides the match makes (τ²+r₁)D(δ_(c)) equal r₁+ψ−1. At r₁=0, the first is τ²D(2ζ′/τ), so if that be larger than ψ−1 then a positive r₁ is needed to bring it down to the matching value. Then {tilde over (r)}_(crit)* is less than τ²D(2ζ′/τ). Whereas if τ²D(2(ζ′/τ) is less than ψ−1 then r_(crit)* is greater than D(2ζ/τ), but not by much as shall be seen. In any case, write {tilde over (r)}_(crit)*/τ² as

$\frac{r_{1}}{\tau^{2}} + \frac{\psi - 1}{\tau^{2}}$

which by Lemma 25 is

$\frac{\psi - 1}{\tau^{2}} + {\left( {1 + {\zeta/\tau}} \right)^{2}^{{- \gamma}/{({1 + {\zeta/\tau}})}^{2}}} - 1.$

Use γ=2ζ′/τ(ψ−1)/τ² and for this proof abbreviate a=(ψ−1)/τ² and b=2ζ′/τ. The exponent γ/(1+ζ/τ)² is then (a+b)/(1+b) and the expression for r_(crit)*/τ² becomes

a+(1+b)e ⁻⁽ a+b)/(1+b)−1.

Add and subtract D(b) in the numerator to write (a+b)/(1+b) as (a−D(b))/(1+b) plus (b+D(b))(1+b), where by the definition of D(b) the latter term is simply log(1+b) which leads to a cancelation of the 1+b outside the exponent. So the above expression becomes

a+e ^((a−D(b))/(1+b))−1,

which is a+e^(−υ)−1=a−υ+excess, where excess=e^(−υ)−(1−υ) and υ=(a−D(b))/(1+b). For a≧D(b), that is, υ≧0, the excess is less than υ²/2, by the second order expansion of e^(−υ), since the second derivative is bounded by 1, which provides the claimed control of the remainder. The a−υ may be written as [D(b)+ba]/(1+b) the average of D(b) and a with weights 1/(1+b) and b/(1+b), or equivalently as D(b)+b(a−D(b))/(1+b). Plugging in the choices of a and b completes the demonstration of Lemma 29.

An implication when ζ and ψ−1 are positive, is that r_(crit)*/τ² is not more than

${D\left( {2{\zeta^{\prime}/\tau}} \right)} + \frac{2{\zeta^{\prime}\left( {\psi - 1} \right)}}{\tau^{3}} + {\frac{\left( {\psi - 1} \right)^{2}}{\tau^{4}}.}$

This bound, and its sharper form in the above lemma, shows that {tilde over (r)}_(crit)*/τ² is not much more than D(2ζ′/τ), which in turn is less than 2(ζ′)²/τ², near 2ζ²/τ².

Also take note of the following monotonicity property of the function ψ(ζ) for ζ≧0. It uses the fact that τ≧1. Indeed, τ≧√{square root over (2 log B)} is at least √{square root over (2 log 2)}=1.18.

Lemma 30.

Monotonicity of ψ: With τ≧1.0, the positive function ψ(z)=(2τ+z)φ(z)/Φ(z) is strictly decreasing for z≧0. Its maximum value is ψ(0)=4τ/√{square root over (2π)}≦1.6τ. Moreover γ=2ζ′/τ+(ψ−1)/τ² is positive.

Demonstration of Lemma 30

The function ψ(z) is clearly strictly positive for z≧0. Its derivative is seen to be

ψ′(z)=−[ψ(z)+z(2τ−z)−1]φ(z)/Φ(z).

Note that the function τ²γ(z) matches the expression in brackets, this derivative equals

−τ²γ(z)φ(z)/Φ(z).

The τ²γ(z) is at least ψ(z)+2τz−1, and it remains to show that it is positive for all z≧0. It is clearly positive for z≧1/2τ. For 0≦z≦1/2τ, lower bound it by lower bounding ψ(z)−1 by 2τφ(1/2τ)/Φ(1/2τ)−1, which is positive provided 1/2τ is less that the unique point z=z_(root)>0 where φ(z)=zΦ(z). Direct evaluation shows that this z is between 0.5 and 0.6. So τ≧1.0 suffices for the positivity of γ(z) and equivalently the negativity of ψ′(z) for all z≧0. This completes the demonstration of Lemma 30.

The monotonicity of ψ(ζ) is associated with decreasing shortfall δ*, as ζ is increased, though with the cost of increasing r_(crit)*. Evaluating r_(crit)* as a function of ζ enables control of the tradeoff.

Remark: The rate R=

/(1−r/τ²) has been parameterized by r. As stated in Lemma 24, the relationship between the gap and r, expressed as gap=(r−r_(crit))/[snr(τ²+r₁)], may also be written r=r_(crit)+snr(τ²+r₁)gap. Recall also that one may set gap=η+ f+1/(m−1), with f=mf*ρ. In this way, the rate parameter r is determined from the choices of ζ that appear in r_(crit) as well as from the parameters m, f and η that control, respectively, the number of steps, the fractions of false alarms, and the exponent of the error probability.

The importance of ζ in this section is that provides for the evaluation of r_(crit) and through r₁ it controls the location of the upper end of the region in which g(x)−x is shown to exceed a target gap. For any ζ, the above remark conveys the smallest size rate drop parameter r for which that gap is shown to be achieved.

In the rate representation R, draw attention to the product of two of the denominator factors (1+D(δ_(c))/snr)(1+r/τ²). Here below these factors are represented in a way that exhibits the dependence on r_(crit)* and the gap.

Using r equal to r_(crit)+snr gap τ²(1−r₁/τ²) write the factor 1+r/τ² as the product (1+r_(crit)/τ²)(1+ξsnr gap) where ξ is the ratio (1+r₁/τ²)/(1+r_(crit)/τ²), a value between 0 and 1, typically near 1. Thus the product (1+D(δ_(c))/snr)(1−r/τ²) takes the form

(1+D(δ_(c))/snr)(1+r _(crit)/τ²)+(1+ξsnr gap).

Recall that (1+D(δ_(c))/snr)(1+r_(crit)/τ²) is equal to

$1 + \frac{\left( {1 + {r_{1}/\tau^{2}}} \right){D\left( \delta_{c} \right)}}{snr} + \frac{r_{crit}^{*}}{\tau^{2}}$

which, at r₁=r_(1,match), is equal to

$1 + \frac{{\overset{\sim}{r}}_{crit}^{*}}{{snr}\; \tau^{2}} + {\frac{{\overset{\sim}{r}}_{crit}^{*} + 1 + \varepsilon}{\tau^{2}}.}$

So in this way these denominator factors are expressed in terms of the gap and {tilde over (r)}_(crit)*, where {tilde over (r)}_(crit)* is near 2ζ² by the previous corollary.

Complete this subsection by inquiring whether r_(crit) is positive for relevant ζ. By the definition of r_(crit), its positivity is equivalent to the positivity of r_(crit)*+r₁D(δ_(c))/snr which is not less than 1+ε+(τ²+r₁)D(δ_(c))+r₁D(δ_(c))/snr. The multiplier of D(δ_(c)) is (τ²+r₁)(1+1/snr) which is positive for r₁≧−τ²snr/(1+snr). So it is asked whether that be a suitable lower bound on r₁. Recall the relationship between x* and r₁,

${1 - x^{*}} = {\frac{r - r_{1}}{{snr}\left( {\tau^{2} + r_{1}} \right)} = {{gap} + {\frac{r_{crit} - r_{1}}{{snr}\left( {\tau^{2} + r_{1}} \right)}.}}}$

Recognizing that r_(crit)−r₁ equals r_(crit)*−r₁ divided by 1+D(δ_(c))/snr, expressing D(δ_(c)) in terms of r_(crit)* and r₁ as above, one can rearrange this relationship to reveal the value of r₁ as a function of x*+gap and r_(crit)*. Using r_(crit)*>0 one finds that the minimal r₁ to achieve positive x*+gap is indeed greater than −τ² snr/(1+snr).

9.8 Determination of ζ:

In this subsection solve, where possible, for the optimal choice of ζ in the expression Δ_(ζ,δ) _(c) which balances contributions to the rate drop with the quantity δ_(mis)* related to the mistake rate. As above it is

Δ_(ζ,δ) _(x) =δ_(mis)*+(1+D(δ_(c))/snr)(1+r _(crit)/τ²)−1.

Also work with the simplified form Δ_(ζ,δ) _(c) _(,simp) in which δ_(mis,simp)* is used in place of δ_(mis)*. For 0≦δ_(c)<snr, this Δ_(ζ,δ) _(c) coincides, as seen herein above, with

${\frac{{\left( {{2\tau} + \zeta} \right){\varphi (\zeta)}} + {rem}}{2\; C\; {\tau^{2}\left( {1 + {\zeta/\tau}} \right)}^{2}} + \frac{\left( {1 + {r_{1}/\tau^{2}}} \right){D\left( \delta_{c} \right)}}{snr} + \frac{r_{crit}^{*}}{\tau^{2}}},$

where rem=[(τ²+r₁)D(δ_(c))(r₁−1)] Φ(ζ). Define Δ_(ζ,δ) _(c) _(,simp) to be the same but without the (1+ζ/τ)² in the denominator of the first part.

Seek to optimize Δ_(ζ,δ) _(c) or Δ_(ζ,δ) _(c) _(,simp) over choices of ζ for each of the quartet of choices of δ_(c) given by 0, δ_(thresh), δ_(match) and snr. The minimum of Δ_(ζ,δ) _(c) provides what is denoted as Δ_(shape) as summarized in the introduction.

Optimum or near optimum choices for ζ are provided for the cases of δ_(c) equal to 0, δ_(match), and snr, respectively. These provide distinct ranges of the signal to noise ratio for which these cases provide the smallest Δ_(ζ,δ) _(c) . At present, the inventors herein have not been able to determine whether the minimum Δ_(ζ,δ) _(thresh) has a range of signal to noise ratios at which its minimum is superior to what is obtained with the best of the other cases. What the inventors can confirm regarding δ_(thresh) is that for small snr the min_(ζ)Δ_(ζ,δ) _(thresh) requires δ_(thresh) near snr, and that for snr above a particular constant, the minimum Δ_(ζ,δ) _(thresh) matches min_(ζ)Δ_(ζ,0) with δ_(thresh)=0 at the minimizing ζ.

Optimal choices for ζ for the cases of δ_(c) equal to 0, δ_(match), and snr, respectively, provide three disjoint intervals R₁, R₂, R₃ of signal to noise ratio. The case of δ_(c)=0 provides the optimum for the high end of snr in R₃; the case of δ_(c)=δ_(match) provides the best bounds for the intermediate range R₂; and the case of δ_(c)=snr provides the optimum for the low snr range R₁.

The tactic is to consider these choices of δ_(c) separately, either optimizing over ζ to the extent possible or providing reasonably tight upper bounds on min_(ζ)Δ_(ζ,δ) _(c) , and then inspect the results to see the ranges of snr for which each is best.

Note directly that Δ_(ζ,δ) _(c) is a decreasing function of snr for the δ_(c)=0 and δ_(c)=δ_(match) cases, so min_(ζ)Δ_(ζ,δ) _(c) also be decreasing in snr. Likewise for Δ_(ζ,δ) _(c) _(,simp).

Remember that log base e is used, so the capacity is measured in mats.

Lemma 31.

Optimization of Δ_(ζ,δ) _(c) _(,simp) with δ_(c)=0. At δ_(c)=0, the Δ_(ζ,0,simp) is optimized at the 1/(2

+1) quantile of the standard normal distribution

ζ=ζ_(C)=Φ⁻¹(1/(2

+1)).

If

>1/2 this ζ_(C) is less than or equal to 0 and min_(ζ)Δ_(ζ,0,simp) is not more than

$\frac{\left( {{2\; C} + 1} \right){\varphi \left( {\zeta \; c} \right)}}{C\; \tau} + {\frac{1}{\tau^{2}}.}$

Dividing the first term by (1+ζ_(C)/τ)² gives an upper bound on min_(ζ)Δ_(ζ,0) valid for ζ_(C)>−τ. The bound is decreasing in

when ζ_(C)>−τ+1. Let

, exponentially large in τ²/2, be such that ζ_(C) _(large) =−τ+1. For

>

_(large), use ζ=−τ+1 in place of ζ_(C), then the first term of this bound is exponentially small in τ²/2 and hence polynomially small in 1/B.

This the ζ=ζ_(C)* advocated for δ_(c)=0 is

ζ_(C)*=max{ζ_(C),−τ+1}.

Examination of the bound shows an implication of this Lemma. When

is large compared to τ the Δ_(shape) is near 1/τ². This is clarified in the following corollary which provides slightly more explicit bounds.

Corollary 32.

Bounding min Δ_(ζ,0) with δ_(c)=0. To upper bound min_(ζ)Δ_(ζ,0,simp), the choice ζ=0 provides

${\frac{\left( {{2\; C} + 1} \right)}{C}\left( {\frac{1}{\tau \sqrt{2\pi}} + \frac{1}{4\tau^{2}}} \right)},$

which also bounds min_(ζ)Δ_(ζ,0). Moreover, when

≧1/2, the optimum ζ_(C) satisfies |ζ_(C)|≦

and provides the following bound, which improves on the ζ=0 choice when

≧2.2,

${\frac{\xi \left( {{\zeta \; c}} \right)}{C\; \tau} + \frac{1}{\tau^{2}}},$

not more than

${\frac{\xi \left( \sqrt{2\; {\log \left( {C + {1/2}} \right)}} \right)}{C\; \tau} + \frac{1}{\tau^{2}}},$

where ξ(z) equals z+/z for z≧1 and equals 2 for 0<ζ<1. Dividing the first term by (1+ζ_(X)/τ)² gives an upper bound on min_(ζ)Δ_(ζ,0) of

$\frac{\xi \left( {{\zeta \; c}} \right)}{C\; {\tau \left( {1 + {\zeta \; {c/\tau}}} \right)}^{2}} + {\frac{1}{\tau^{2}}.}$

When B>1+snr, this bound on min_(ζ)Δ_(ζ,0) improves on the bound with ζ=0, for

≧5.5. As before, when

≧

_(large) for which ζ_(C) _(large) =−τ+1, use the bound with

_(large) in place of

.

The min_(ζ)Δ_(ζ,0,simp) bound above is smaller than given below for min_(ζ,δ) _(match) _(,simp), when the snr is large enough that an expression of order

/(log

)^(3/2) exceeds τ.

The quantity d=d_(snr)=2

/ν=(1+1/snr) log(1+snr) has a role in what follows. It is an increasing function of snr, with value always at least 1.

For 2

/ν≧τ/√{square root over (2π)} use non-positive ζ, whereas for 2

/ν<τ/√{square root over (2π)} use positive ζ. Thus the discriminant of whether to use positive ζ is the ratio ψ=d/τ and whether it is smaller than 1/√{square root over (2π)}. This ratio ω is

$\omega = {\frac{d}{\tau} = {\frac{2\; C}{v\; \tau}.}}$

In the next two lemma use δ_(c)=δ_(match). Using the results of Lemma 25 and 1+1/snr=1/ν, the form of Δ_(ζ,δ) _(match) simplifies to

$\frac{\psi}{2\; C\; {\tau^{2}\left( {1 + {\zeta/\tau}} \right)}^{2}} + {\frac{1}{v}\frac{{\overset{\sim}{r}}_{crit}^{*}}{\tau^{2}}} + {\frac{1 + \varepsilon}{\tau^{2}}.}$

Recall for negative ζ that ψ is near 2τ|ζ| and {tilde over (r)}_(crit)* through γ²/2 is near 2/|ζ|², with associated bounds given in Lemma 27. So it is natural to set a negative ζ that minimizes −τζ/

+2/(νζ²τ²) for which the solution is

ζ=−(4

/ντ)^(1/3)=−(2ω)^(1/3),

which is here denoted as ζ_(1/3).

Lemma 33.

Optimization of Δ_(ζ,δ) _(c) at δ_(c)=δ_(match): Bounds from non-positive ζ. The choice of ζ=0 yields the upper bound on min_(ζ)Δ_(ζ,δ) _(match) of

$\frac{2}{C\; \tau \sqrt{2\pi}} + \frac{4}{v\; \tau^{2}\pi} + {\frac{1 + \varepsilon}{\tau^{2}}.}$

As for negative ζ, the choice ζ=ζ_(1/3)=−(4

/ντ)^(1/3) yields the upper bound on min_(ζ)Δ_(ζ,δ) _(match) of

${\frac{1}{\left( {1 + {\zeta_{1/3}/\tau}} \right)^{2}}\left( {\frac{2.4}{v^{1/3}C^{2/3}\tau^{4/3}} + \frac{2}{{\zeta_{1/3}}C\; \tau}} \right)} + {\frac{1 + \varepsilon}{\tau^{2}}.}$

Amongst the bounds so far with ζ≦0, the first term controlling δ_(mis)* is smallest at ζ=0 where it is 2/[

τ√{square root over (2π)}]. The advantage of going negative is that then the 4/[ντ²π] term is replaced by terms that are smaller for large

.

Comparison:

The two bounds in Lemma 33 may be written as

${\frac{4}{v\; \tau^{2}}\left\lbrack {\frac{1}{\sqrt{2\pi}\omega} + \frac{1}{\pi}} \right\rbrack} + \frac{1 + \varepsilon}{\tau^{2}}$ and ${{\frac{4}{v\; \tau^{2}}\left\lbrack {\frac{1.5}{\left( {2\omega} \right)^{2/3}} + \frac{2}{\left( {2\omega} \right)^{4/3}}} \right\rbrack} + \frac{1 + \varepsilon}{\tau^{2}}},$

respectively, neglecting the (1+ζ_(1/3)/τ) factor. Numerical comparison of the expressions in the brackets reveals that the former, from ζ=0, is better for ω<5.37, while the later from ζ=ζ_(1/3) is better for ω≧5.37, which is for |ζ_(1/3)|≧2.2.

Next compare the leading term of the bound ζ_(1/3) and δ_(c)=δ_(match) to the corresponding part of the bound using ζ_(c) and δ_(c)=0. These are, respectively,

$\frac{2.4}{v^{1/3}C^{2/3}\tau^{4/3}}$ and $\frac{\xi \left( {{\zeta \; c}} \right)}{C\; \tau}.$

From this comparison the δ_(c)=0 solution is seen to be better when

$\frac{4.5\; C}{{v\left( {\xi \left( {{\zeta \; c}} \right)} \right)}^{3}} > {\tau.}$

Modified to take into account the factors 1+∂_(1/3)/τ and 1+ζ_(c)*/τ, this condition defines the region R₃ of very large snr for which δ_(c)=0 is best. To summarize it corresponds to snr large enough that an expression near 4.5

/(log

)^(3/2) exceeds τ, or, equivalently, that

is at least a value of order τ(log τ)^(3/2), near to (τ/4.5)(log(τ/4.5))^(1/5), for sufficient size τ.

Next consider the case of ω=d/τ less than 1/√{square root over (2π)} for which positive ζ is used. The function φ(ζ)/Φ(ζ) is strictly decreasing. From its inverse, let ζ_(ω) be the unique value at which φ(ζ)/Φ(ζ)=2ω. It is used to provide a tight bound on the optimal Δ_(ζ,δ) _(match) .

Lemma 34.

Optimization of Δ_(ζ,δ) _(c) at δ_(c)=δ_(match): Bounds from positive ζ. Consider the case that τ/√{square root over (2π)}≧2

/ν. Let ω=2

/ντ. The choice of ζ=ζ_(ω)yields Δ_(ζ,δ) _(match) not more than

${\frac{2}{v\; \tau^{2}}\left\lbrack {2 + \left( {\zeta_{\omega} + {2\omega}} \right)^{2}} \right\rbrack} + {\frac{1 + \varepsilon}{\tau^{2}}.}$

This ζ_(ω) is not more than

$\zeta_{\omega}^{*} = \sqrt{2\mspace{11mu} {\log \left( {{1/2} + {{1/2}\omega \sqrt{2\pi}}} \right)}}$

which is

$\sqrt{{2\mspace{11mu} {\log \left( {\frac{1}{2} + \frac{\tau \; v}{4\sqrt{2\pi}}} \right)}},}$

at which Δ_(ζ,δ) _(match) is not more than

${\frac{2}{v\; \tau^{2}}\left\lbrack {2 + \left( {\sqrt{2{\log \left( {\frac{1}{2} + \frac{\tau \; v}{4\sqrt{2\pi}}} \right)}} + \frac{4}{\tau \; v}} \right)^{2}} \right\rbrack} + {\frac{1 + \varepsilon}{\tau^{2}}.}$

For small d/τ the 2ω=4

/ντ=2d/τ term inside the square is negligible compared to the log term. Then the bound is near

$\left. {\frac{4}{v\; \tau^{2}}\left\lbrack {1 + {\log \left( {\frac{1}{2} + \frac{\tau}{2d\sqrt{2\pi}}} \right)} + \frac{4}{\tau \; v}} \right)}^{2} \right\rbrack + {\frac{1}{\tau^{2}}.}$

In particular if snr is small the d=2

/ν is near 1 and the bound is near

${\frac{4}{v\; \tau^{2}}\left\lbrack {1 + {\log \left( {\frac{1}{2} + \frac{\tau}{2\sqrt{2\pi}}} \right)}} \right\rbrack} + {\frac{1}{\tau^{2}}.}$

Finally, consider the case δ_(c)=snr. The following lemma uses the form of Δ_(ζ,snr) given in the remark following Lemma 26.

Lemma 35.

Optimization of Δ_(ζ,δ) _(c) at δ_(c)=snr. The Δ_(ζ,snr) is the maximum of the expressions

${\overset{\_}{\Phi}(ϛ)} + {\frac{\left( {1 + {{D({snr})}/{snr}}} \right)}{\left( {1 + {snr}} \right)}\left( {1 + {{snr}{\overset{\_}{\Phi}(ϛ)}}} \right)\left( {1 + {ϛ/\tau}} \right)^{2}} - 1$ and ${\overset{\_}{\Phi}(ϛ)} + {{D({snr})}/{{snr}.}}$

The first expression in this max is approximately of the form b Φ(ζ)+2ζ/τ+c, optimized at

${ϛ = \sqrt{2\mspace{11mu} {\log \left( {{{\tau \left( {1 + {2}} \right)}/2}\sqrt{2\pi}} \right)}}},$

where b=1+2

and c is equal to the negative value (1/snr)log(1+snr)−1, at which Φ(ζ)≦φ(ζ)=2/(τb). This yields a bound for that expression near

${\frac{2}{\tau} + \frac{2\sqrt{2\mspace{11mu} {\log \left( {{{\tau \left( {1 + {2}} \right)}/2}\sqrt{2\pi}} \right)}}}{\tau} + c},$

with which one takes the maximum of it and

$\frac{2}{\tau \left( {1 + {2}} \right)} + {\frac{D({snr})}{snr}.}$

Recall that D(snr)/snr≦snr/2. Because of the D(srr)/snr term the Δ_(ζ,snr) is small only when snr is small. In particular Δ_(ζ,snr) is less than a constant time √{square root over (log τ)}/τ when snr is less than such.

In view of the ν=snr/(1+snr) factor in the denominator of Δ_(ζ,δ) _(match) , one sees that min_(ζ)Δ_(ζ,snr) provides a better bound than Δ_(ζ,δ) _(match) for snr less than a constant times √{square root over (log τ)}/τ.

Demonstration of Lemma 31 and its Corollary:

This Lemma concerns the optimization of ζ in the case δ_(c)=0. In this case 1+r₁/τ²=(1+ζ/τ)², the role of r_(crit)* is played by r_(up) and the value of Δ_(ζ,0,simp) is

${\frac{1}{2}\frac{{\left( {{2\tau} + ϛ} \right){\varphi (ϛ)}} + {rem}}{\tau^{2}}} + {\frac{r_{1} + {\left( {{2\tau} + ϛ} \right){\varphi (ϛ)}} + {rem}}{\tau^{2}}.}$

Here rem=−(r₁−1) Φ(ζ), with r₁=2ζτ+ζ². Direct evaluation at ζ=0 gives a bound, at which r₁=0 and rem=1/2.

Let's optimize Δ_(ζ,0,simp) for the choice of ζ. The derivative of (2τ+ζ)φ(ζ)+rem with respect to ζ is seen to simplify to −2(τ+ζ) Φ(ζ). Accordingly, Δ_(ζ,0,simp) has derivative

${2\left( {\tau + ϛ} \right)\left( {1 - {\left( {\frac{1}{2} + 1} \right){\overset{\_}{\Phi}(ϛ)}}} \right)},$

which is 0 at ζ solving Φ(ζ)=2

/(2

+1), equivalently, Φ(ζ=1/(2

+1). At this ζ, the quantities multiplying r₁ including the parts from the two occurrences of the remainder remainder are seen to cancel, such that the resulting value of Δ_(ζ,0,simp) is

$\frac{{\left( {1 + {{1/2}}} \right)\left( {{2\tau} + {ϛ\; c}} \right){\varphi \left( {ϛ\; c} \right)}} + 1}{\tau^{2}}.$

With 2

>1, this ζ=ζ_(C) is negative, so Δ_(ζ,0,simp) is not more than

$\frac{\left( {{2} + 1} \right){\varphi \left( {ϛ\; c} \right)}}{\tau} + {\frac{1}{\tau^{2}}.}$

Per the inequality in the appendix for negative ζ, the φ(ζ_(C)) is not more than the value) ξ(|ζ_(C)|)Φ(ζ)=ξ(|ζ_(C)|)/(2

+1), with ξ(|ζ|) the nondecreasing function equal to 2 for |ζ|≦1 and equal to |ζ|+1/|ζ| for |ζ| greater than 1. So at ζ=ζ_(C), the Δ_(ζ,0,simp) is not more than

${\frac{\xi \left( {{ϛ\; c}} \right)}{\tau} + \frac{1}{\tau^{2}}},$

where from 1/(2

+1)=Φ(ζ_(C))≦(½)e^(−ζ) ^(C) ² ^(/2) it follows that |ζ_(C)|≦

The coefficient ξ

improves on the (2

+1)/√{square root over (2π)} from the ζ=0 case when (2

+1)/2 is less than the value υal for which ξ(√{square root over (2 log υal)})=(2/√{square root over (2π)})υal. Evaluations show υal to be between 2.64 and 2.65. So it is an improvement when 2

≧2υal−1=4.3, and

≧2.2 suffices. The improvement is substantial for large

.

Dividing the first term by (1+ζ_(C)/τ)² produces an upper bound on Δ_(ζ,0) when ζ_(C)>−τ. Exact minimization of Δ_(ζ,0) is possible, though it does not provide an explicit solution. Accordingly, instead use the ζ_(C) that optimizes the simpler form and explore the implications of the division by (1+ζ_(C)/τ)².

Consider determination of conditions on the size

such that the bound on min_(ζ)Δ_(ζ,0) is an improvement over the ζ=0 choice. One can arrange the |ζ_(C)|/τ to be small enough that the factor (1+ζ_(C)/τ)² in the denominator remains sufficiently positive. At ζ=ζ_(C), the bound on ζ_(C)| of

is kept less than τ=√{square root over (2 log B)}(1+δ_(a)) when B is greater than

, and |ζ_(C)|/τ is kept small if B is sufficiently large compared to

.

In particular, suppose B≧l+snr, then τ²/4 is at least

=(1/2)log(1+snr), that is,

, and (1+ζ/τ) is greater than 1−

, which is positive for all

≧1/2. Then for the non-zero ζ_(C) bound on Δ_(ζ,0) to provide improvement over the ζ=0 bound it is sufficient that

be at least the value

₀ at which (2

+1)/√{square root over (2π)} equals ξ(

divided by [1−2

]². Numerical evaluation reveals that

₀ is between 5.4 and 5.45.

Next, consider what to do for very large

for which τ+ζ_(C) is either negative or not sufficiently positive to give an effective bound. This could occur if snr is large compared to B. To overcome this problem, let

_(large) be the value with ζ_(Capacity) _(large) =τ+1. For

≧

_(large), use this ζ=ζ_(C) _(large) in place of ζ_(C) so that τ+ζ=1 stays away from 0. Then upper bound Δ_(ζ,0) by replacing the appearance of

with

_(large). This

_(large) has

≧|ζ|=τ−1 so that

2

_(large)+1≧2e ^((τ−1)) ² ^(/2).

More stringently,

${\frac{1}{{2_{large}} + 1} = {{\Phi (\zeta)} = {{\Phi \left( {\tau - 1} \right)} \leq {\frac{1}{\tau - 1}{\varphi \left( {\tau - 1} \right)}}}}},$

from which 2

_(large)+1 is at least (τ−1)√{square root over (2π)}e^((τ−)) ² ^(/2). Then for

≧

_(large), at ζ=ζ_(C) _(large) the term

$\frac{{\tau\xi}\left( {\zeta } \right)}{{\left( {\tau + \zeta} \right)}\,^{2}}$

is less than

$\frac{2\tau^{2}}{{\left( {\tau - 1} \right)\sqrt{2\pi}^{{({\tau - 1})}^{2}/2}} - 1}$

which is exponentially small in τ²/2 and hence of order 1/B to within a log factor. Consequently, for such very large

, this term is negligible compared to the 1/τ².

Finally, consider the matter of the range of

for which the expression in the first term (2

+1)φ(ζ_(C))/[

τ(1+ζ_(C)/τ)²] is decreasing in

even with the presence of the division by (1+ζ_(C)/τ)². Taking the derivative of this expression with respect to

, one finds that there is a

_(crit), with value of ζ_(C) _(crit) not much greater than −τ, such that the expression is decreasing for

up to

_(crit), after which, for larger

, it becomes preferable to use ζ=ζ_(C) _(crit) in place of ζ_(C), though the determination of

_(crit) is not explicit. Nevertheless, one finds that at

=

_(large) where ζ_(C)=−τ+1, the derivative of the indicated expression is still negative and hence

_(large)≦

_(crit). Thus the obtained bound is monotonically decreasing for

up to

_(large), and thereafter the bound for the first term is negligible. This completes the demonstration of Lemma 31 and its corollary.

Demonstration of Lemma 33:

Recall for negative ζ that ψ is bounded by 2τ[|ζ|+1/|ζ|]. Likewise {tilde over (r)}_(crit)*/τ² is bounded by γ²/[2(1+ζ/τ)²]. Using γ≦2/|ζ|τ this yields {tilde over (r)}_(crit)*/τ² less than 2/[ζ²(τ+ζ)²]. Plugging in the chosen ζ=ζ_(1/3) produces the claimed bound for that case. Likewise directly plugging in ζ=0 into the terms of Δ_(ζ,δ) _(match) provides a bound for that case. This completes the demonstration of Lemma 33.

Demonstration of Lemma 34:

As previously developed, at δ_(c)=δ_(match), the form of Δ_(ζ,δ) _(match) simplifies to

$\frac{\psi}{2{{\tau}^{2}\left( {1 + {\zeta/\tau}} \right)}^{2}} + {\frac{1}{v}\frac{{\overset{\sim}{r}}_{crit}^{*}}{\tau^{2}}} + {\frac{1 + \varepsilon}{\tau^{2}}.}$

Now by Corollary 28, with ζ>0,

{tilde over (r)} _(crit)*≦2(ζ+φ(ζ)/Φ(ζ))².

Also ψ(ζ)=2τ(1+ζ/2τ)φ(ζ)/Φ(ζ) and the (1+ζ/2τ) factor is canceled by the larger (1+ζ/τ)² in the denominator. Accordingly, Δ_(ζ,δ) _(match) has the upper bound

$\frac{\varphi (\zeta)}{{\tau\Phi}(\zeta)} + {\frac{2}{v\; \tau^{2}}\left( {\zeta + \frac{\varphi (\zeta)}{\Phi (\zeta)}} \right)^{2}} + {\frac{1 + \varepsilon}{\tau^{2}}.}$

Plugging in ζ=ζ_(ω) for which φ(ζ)Φ(ζ)=2ω produces the claimed bound.

$\frac{2\omega}{\tau} + {\frac{2}{v\; \tau^{2}}\left( {\zeta_{\omega} + {2\omega}} \right)^{2}} + {\frac{1 + \varepsilon}{\tau^{2}}.}$

To produce an explicit upper bound on Δ_(ζ,δ) _(match) replace the Φ(ζ) in the denominator with its lower bound 1−√{square root over (2π)}φ(ζ)/2, for ζ≧0. This lower bound agrees with Φ(ζ) at ζ=0 and in the limit of large ζ. The resulting upper bound on Δ_(ζ,δ) _(match) is

$\frac{\varphi\zeta}{{\tau}\left( {1 - {\sqrt{2\pi}{{\varphi (\zeta)}/2}}} \right)} + {\frac{2}{v\; \tau^{2}}\left( {\zeta + \frac{\varphi (\zeta)}{\left( {1 - {\sqrt{2\pi}{{\varphi (\zeta)}/2}}} \right)}} \right)^{2}}$ plus  (1 + ε)/τ².

The bound on φ(ζ)/Φ(ζ) of φ(ζ)/(1−√{square root over (2π)}φ(ζ)/2) is found to equal 2ω when √{square root over (2π)}φ(ζ) equals 2/[1+1/ω√{square root over (2π)}], at which ζ=ζ* is

$\zeta^{*} = \sqrt{2{{\log \left( {\frac{1}{2} + \frac{1}{2\omega \sqrt{2\pi}}} \right)}.}}$

Accordingly, this ζ* upper bounds ζ_(ω) and the resulting bound on Δ_(ζ*,δ) _(match) is

$\frac{2\omega}{\tau} + {\frac{2}{v\; \tau^{2}}\left( {\zeta^{*} + {2\omega}} \right)^{2}} + {\frac{1 + \varepsilon}{\tau^{2}}.}$

Using 2ω=4/ντ it is

${\frac{2}{v\; \tau^{2}}\left\lbrack {2 + \left( {\zeta + {2\omega}} \right)^{2}} \right\rbrack} + {\frac{1 + \varepsilon}{\tau^{2}}.}$

This completes the demonstration of Lemma 34.

To provide further motivation for the choice ζ_(ψ), the derivative with respect to ζ of the above expression bounding Δ_(ζ,δ) _(match) for ζ≧0 is seen, after factoring out (4/ν)(ζ+φ/Φ), to equal

${1 - {\frac{1}{2\omega}\frac{\varphi}{\Phi}} - {\left( {\zeta + \frac{\varphi}{\Phi}} \right)\frac{\varphi}{\Phi}}},$

where the last term is negligible if ζ is not small. The first two yield 0 at ζ=ζ_(ψ). Some improvement arises by exact minimization. Set the derivative to 0 including the last term, noting that it takes the form of a quadratic in φ/Φ. Then at the minimizer, φ/Φ equals [√{square root over ((ζ+1/2ω)²+4)}−(ζ+1/2ω)]/2 which is less than 1/(ζ+1/2ω)≦2ω.

For further understanding of the choice of ζ, note that for ζ not small, Φ(ζ) is near 1 and the expression to bound is near φ(ζ)/(

τ)+2ζ²/ντ², which by analysis of its derivative is seen to be minimized at the positive ζ for which φ(ζ) equals 4

/ντ=2ω. It is ζ₁=

$\zeta_{1} = \sqrt{2\log \; {1/{\left( {2\omega \sqrt{2\pi}} \right).}}}$

One sees that ζ* is similar to ζ₁, but has the addition of ½ inside the logarithm, which is advantageous in allowing ω up to 1/√{square root over (2π)}. The difference between the use of ζ* and ζ ₁ is negligible when they are large (i.e. when ω is small), nevertheless, numerical evaluation of the resulting bound shows ζ* to be superior to ζ₁ for all ω≦1/√{square root over (2π)}.

In the next section the rate expression is used to solve for the optimal choices of the remaining parameters.

10 Optimizing Parameters for Rate and Exponent

In this section the parameters are determined that maximize the communication rate for a given error exponent. Moreover, in the small exponent (large L) case, the rate and its closeness to capacity are determined as a function of the section size B and the signal to noise ratio snr.

Recall that the rate of the sparse superposition inner code is

${R = \frac{\left( {1 - h^{\prime}} \right)}{\left( {1 + \delta_{a}} \right)^{2}\left( {1 + \delta_{sum}^{2}} \right)\left( {1 + {r/\tau^{2}}} \right)}},$

with (1−h′)=(1−h)(1−h_(f)). The inner code makes a weighted fraction of section mistakes bounded by δ_(m)=δ*+η+ f with high probability, as shown previously herein. If one multiply the weighted fraction by the factor 1/[L min_(l)π_((l))] which equals fac=snr(1+δhd sum²)/[2

(1+δ_(c))], then it provides an upper bound on the (unweighted) fraction of mistakes δ_(mis)=facδ_(m) equal to

δ_(mis) =fac(δ*+η+ f).

So with the Reed-Solomon outer code of rate 1−δ_(mis), which corrects the remaining fraction of mistakes, the total rate of the code is

$R_{tot} = {\frac{\left( {1 - \delta_{mis}} \right)\left( {1 - h^{\prime}} \right)}{\left( {1 + \delta_{sum}^{2}} \right)\left( {1 + \delta_{a}} \right)^{2}\left( {1 + {r/\tau^{2}}} \right)}.}$

This multiplicative representation is appropriate considering the manner in which the contributions arise. Nevertheless, in choosing the parameters in combination, it is helpful to consider convenient and tight lower bounds on this rate, via an additive expression of rate drop from capacity.

Lemma 36.

Additive representation of rate drop: With a non-negative value for r, represented as in Remark 3 above, the rate R_(tot) is at least (1−Δ)

with Δ given by

$\Delta = {\frac{{snr}\; \delta^{*}}{\left( {1 + \delta_{c}} \right)2} + \frac{r_{crit}^{*}}{\tau^{2}} + \frac{\left( {1 + {r_{1}/\tau^{2}}} \right){D\left( \delta_{c} \right)}}{snr} + {\frac{snr}{2}\left( {\eta + \overset{\_}{f}} \right)} + {{snr}\mspace{14mu} {gap}} + h_{f} + h + {2\delta_{a}} + {\frac{2}{L\; v}.}}$

These are called, respectively, the first and second lines of the expression for Δ. The first line of Δ is what is also denoted in the introduction as Δ_(shape) or in the previous section as Δ_(ζ) to emphasize its dependence on ζ which determines the values of r₁, δ_(c), and δ*. In contrast the second line of Δ, which is denote Δ_(second), depends on η, f, and a. It has the ingredients of Δ_(alarm) and the quantities which determine the error exponent.

Demonstration of Lemma 36:

Consider first the ratio

$\frac{1 - \delta_{mis}}{\left( {1 + \delta_{sum}^{2}} \right)\left( {1 + {r/\tau^{2}}} \right)}.$

Splitting according to the two terms of the numerator and using the non-negativity of r it is at least

$\frac{1}{\left( {1 + \delta_{sum}^{2}} \right)\left( {1 + {r/\tau^{2}}} \right)} - {\frac{\delta_{mis}}{1 + \delta_{sum}^{2}}.}$

From the form of fac, the ratio δ_(mis)/(1+δ_(sum) ²) subtracted here is equal to

${\frac{snr}{2}\frac{\left( {\delta^{*} + \eta + \overset{\_}{f}} \right)}{\left( {1 + \delta_{c}} \right)}},$

where in bounding it further drop the (1+δ_(c)) from the terms involving η+ f, but find it useful to retain the term involving δ*.

Concerning the factors of the first part of the above difference, use δ_(sum) ²≦D(δ_(c))/snr+2

/Lν to bound the factor (1+δ_(sum) ²) by

(1+D(δ_(c))/snr)(1+2

/Lν).

sqand use the representation of (1+D(δ_(c))/snr)(1+r/τ²) developed at the end of the previous section,

$\left( {1 + \frac{\left( {1 + {r_{1}/\tau^{2}}} \right){D\left( \delta_{c} \right)}}{snr} + \frac{r_{crit}^{*}}{\tau^{2}}} \right)\left( {1 + {\xi \; {snr}\mspace{14mu} {gap}}} \right)$

to obtain that the first part of the above difference is at least

$1 - {\left\lbrack {\frac{r_{crit}^{*}}{\tau^{2}} + \frac{\left( {1 + {r_{1}/\tau^{2}}} \right){D\left( \delta_{c} \right)}}{snr} + {{snr}\mspace{14mu} {gap}} + \frac{2}{L\; v}} \right\rbrack.}$

Proceed in this way, including also the factors (1−h′) and 1/(1+δ_(a)) to produce the indicated bound on the rate drop from capacity. This bound is tight when the individual terms are small, because then the products are negligible in comparison. Here it is used that 1/(1+δ_(i))≧1−δ_(i) and (1−δ₁)(1−δ₂) exceeds 1−δ₁−δ₂, for non-negative reals δ_(i), where the amount by which it exceeds is the product δ₁δ₂. Likewise inductively products π_(i)(1−δ_(i)) exceed 1−Σ_(i)δ_(i). This completes the demonstration of Lemma 36.

This additive form of Δ provides some separation of effects that facilitates joint optimization of the parameters as in the next Lemma. Nevertheless, once the parameters are chosen, it is preferable to reexpress the rate in the original product form because of the slightly larger value it provides.

Let's recall parameters that arise in this rate and how they are interrelated. For the incremental false alarm target use

${f^{*} = {\frac{1}{\sqrt{2\pi}\sqrt{2\log \; B}}^{{- a}\sqrt{2\log \; B}}}},$

such that

$\delta_{a} = {\frac{\log \; {1/\left\lbrack {f^{*}\sqrt{2\pi}\sqrt{2\log \; B}} \right\rbrack}}{2\; \log \; B}.}$

With a number of steps m at least 2 and with ρ at least 1, the total false alarms are controlled by f=mf*ρ and the exponent associated with failed detections is determined by a positive η. Set h_(f) equal to 2snr f plus the negligible ε₃=2sgr√{square root over ((1+snr)k/L_(π))}+snr/L_(π), arising in the determination of the weights of combination of the test statistic. To control the growth of correct detections set

gap=η+ f+1/(m−1).

The r₁, r_(crit)*, δ* and δ_(c) are determined as in the preceding section as functions of the positive parameter ζ.

The exponent of the error probability e^(−L) ^(n) ^(ε) is ε=ε_(η) either given by

ε_(η)=2η²

or, if the Bernstein bound is used, by

$\frac{1}{2}\frac{L}{L_{\pi}}\frac{\eta^{2}}{V + {\left( {1/3} \right)\eta \; {L/L_{\pi}}}}$

where V is the minimum value of the variance function discussed previously. For the chosen power allocation the L_(π)=1/max_(l)π_((l)) has L/L_(π) equal to (2

/ν)(1+δ_(sum) ²), which may be replaced by its lower bound (2

/ν) yielding

$ɛ_{\eta} = {\frac{\eta^{2}}{{V\; {v/}} + {\left( {2/3} \right)\eta}}.}$

In both cases the relationship between ε and η is strictly increasing on η>0 and invertible, such that for each ε≧0 there is a unique corresponding η(ε)≧0.

Set the Chi-square concentration parameter h so that the exponent (n−m+1)/h_(m) ²/2 matches L_(π)ε_(η), where h_(m) equals (nh−m+1)(n−m+1). Thus h_(m)=√{square root over (2ε_(η)L_(π)/(n−m+1))} which means

h=(m−1)/n+√{square root over (2ε_(η) L _(π)(n−m+1))}/n.

With L_(π)≦(ν/2

)L not more than (ν/2)n/log B, it yields h not more than (m−1/n+h* where

h*=√{square root over (νε_(η)/log B)}.

The part (m−1)/n which is (m−1)

/L log B is lumped with the above-mentioned remainders 2

/Lν and ε₃, as negligible for large L.

Finally, ρ>1 is chosen such that the false alarm exponent f

(ρ)/ρ matches ε_(η). The function

(ρ)/ρ=log ρ−1+1/ρ is 0 at ρ=1 and is an increasing function of ρ≧1 with unbounded positive range, so it has an inverse function ρ(ε) at which set ρ=ρ(ε_(η)/ f).

Herein the inventors pin down as many of these values as one can by exploring the best relationship between rate and error probability achieved by the analysis of the invented decoder.

Take advantage of the decomposition of Lemma 36.

Lemma 37.

Optimization of the second line of Δ. For any given positive η providing the exponent ε_(η) of the error probability, the values of the parameters m, f, and ρ, are specified to optimize their effect on the communication rate. The second line Δ_(second) of the total rate drop (

−R)/

bound Δ is the sum of three terms

Δ_(m)+Δ _(f) +Δ_(η),

plus the negligible Δ_(L)=2

/(Lν)+(m−1)

/(L log B)+ε₃. Here

$\Delta_{m} = {\frac{snr}{m - 1} + \frac{\log \; m}{\log \; B}}$

is optimized at a number of steps m equal to an integer part of 2+snr log B at which Δ_(m) is not more than

$\frac{1}{\log \; B} + {\frac{\log \left( {2 + {{snr}\; \log \; B}} \right)}{\log \; B}.}$

Likewise Δ _(f) is given by

${{{{snr}\left( {3 + {{1/2}}} \right)}\overset{\_}{f}} - \frac{\log \left( {\overset{\_}{f}\sqrt{2\pi}\sqrt{2\log \; B}} \right)}{\log \; B}},$

optimized at the false alarm level f=1/[snr(3+1/2

)log B] at which

$\Delta_{\overset{\_}{f}} = {\frac{1}{\log \; B} + {\frac{\log \left( {{{snr}\left( {3 + {{1/2}\; C}} \right)}{\sqrt{\log \; B}/\sqrt{4\pi}}} \right)}{\log \; B}.}}$

The Δ_(η) is given by

$\Delta_{\eta} = {{\eta \; {{snr}\left( {1 + {{1/2}\; C}} \right)}} + \frac{\log \; \rho}{\log \; B} + h^{*}}$

evaluated at the optimal ρ=ρ(ε_(η)/ f). It yields Δ_(η) not more than

ηsnr(1+1/2

)+ε_(η) snr(3+1/2

)+1/log B+h*.

Together the optimized Δ_(m)+Δ_(f) form what is called Δ_(alarm) in the introduction. In the next lemma use the Δ_(η) expression, or its inverse, to relate the error exponent to the rate drop.

Demonstration of Lemma 42:

Recall that

${2\delta_{a}} = {\frac{\log \left\lbrack {\rho \; {m/\left( {\overset{\_}{f}\sqrt{2\pi}\sqrt{2\; \log \; B}} \right)}} \right\rbrack}{\log \; B}.}$

The log of the product is the sum of the logs. Associate the term log m/log B with Δ_(m) and the term log ρ/log B with Δ_(η) and leave the rest of 2δ_(a) as part of Δ _(f) . The rest of the terms of Δ associate in the obvious way. Decomposed in this way, the stated optimizations of Δ_(m) and Δ_(f) are straightforward.

For Δ_(m)=snr/(m−1)+(log m)/(log B) consider it first as a function of real values m≧2. Its derivative is −snr(m−1)²+1/(m log B), which is negative at m₁=1+snr log B, positive at m₂=2+snr log B, and equal to 0 at a point m₂*=[m₂+√{square root over (m₂ ²−4)}]/2 in between m₁ and m₂. Moreover, the value of Δ_(m) ₂ is seen to be smaller than the value of m₁. Accordingly, for m in the interval m₁<m≦m₂, which includes an integer value, the Δ_(m) remains below what is attained for m≦m₁. Therefore, the minimum among integers occurs at either at the floor └2+snr log B┘ or at the ceiling ┌2+snr log B┐ of m₂, whichever produces the smaller Δ_(m). [Numerical evaluation confirms that the optimizer tends to coincide with the rounding of m₂* to the nearest integer, coinciding with a near quadratic shape of Δ_(m) around m₂*, by Taylor expansion for m not far from m₂*.]

When the optimal integer in is less than or equal to rn₂=2+snr log B, use that it exceeds M₁ to conclude that Δ_(m)≦1/log B+(log m₂)/(log B). When the optimal m is a rounding up of Trt₂, use snr/(m−1)≦snr/(1+snr log B). Also log m exceeds log m₂ by the amount log(m/m₂)≦log(1+1/m₂) less than 1/(1+snr log B), to obtain that at the optimal integer, Δ_(m) remains less than

$\frac{1}{\log \; B} + {\frac{\log \; M_{2}}{\log \; B}.}$

For Δ _(f) and Δ_(η) there are two ways to proceed. One is to use the above expression for δ_(a), and set Δ _(f) as indicated, which is easily optimized by setting f at the value specified.

For Δ_(η) note that the log ρ/log B has numerator log ρ equal to 1−1/ρ+ε_(η)/ f at the optimized ρ, and accordingly get the claimed upper bound by dropping the subtraction of 1/ρ. This completes the demonstration of Lemma 42.

It is noted that in accordance with the inverse function ρ(ε_(η)/ f) there is an indirect dependence of the rate drop on f when ε_(η)>0. One can jointly optimize Δ _(f) +Δ_(η) for f for given η, though there is not explicit formula for that solution. The optimization claimed is for Δ _(f) , which produces a clean expression suitable for use with small positive η.

A closely related presentation is to write

${2\delta_{a}} = \frac{\log \left\lbrack {m/\left( {{\overset{\_}{f}}^{*}\sqrt{2\pi}\sqrt{2\; \log \; B}} \right)} \right\rbrack}{\log \; B}$

and in other terms involving f, write it as p f*. Optimization of

${{{{snr}\left( {3 + {{1/2}\; C}} \right)}\rho \; {\overset{\_}{f}}^{*}} - \frac{\log \left( {{\overset{\_}{f}}^{*}\sqrt{2\pi}\sqrt{2\; \log \; B}} \right)}{\log \; B}},$

occurs at a baseline false alarm level f* that is equal to 1/[ρsnr(3+1/2

)log B]. These approaches have the baseline level of false alarms (as well as the final value of δ_(a)) depending on the subsequent choice of ρ.

One has a somewhat cleaner separation in the story, as in the introduction, if f* is set independent of ρ. This is accomplished by a different way of spitting the terms of Δ_(second). One writes f=ρ f* as f*+(ρ−1) f*, the baseline value plus the additional amount required for reliability. Then set Δ _(f*) to equal

${{{{snr}\left( {3 + {{1/2}\; C}} \right)}{\overset{\_}{f}}^{*}} - \frac{\log \left( {{\overset{\_}{f}}^{*}\sqrt{2\pi}\sqrt{2\; \log \; B}} \right)}{\log \; B}},$

optimized at f*=1/[snr(3+1/2

)log B], which determines a value of δ_(a) for the rate drop envelope independent of η. In that approach one replaces Δ_(η) with

ηsnr(1+1/2

)+(ρ−1)snr(3+1/2

)+h*,

with ρ defined to solve f*

(ρ)=ε_(η). There is not an explicit solution to the inverse of

(ρ) at ε_(η)/ f*. Nevertheless, a satisfactory bound for small η is obtained by replacing

(ρ) by its lower bound 2(√{square root over (ρ)}−1)², which can be explicitly inverted. Perhaps a downside is that from the form of the f* which minimizes Δ _(f*) one ends up, multiplying by ρ, with a final f larger than before.

With 2(√{square root over (ρ)}−1)² replacing

(ρ), it is matched to 2η²/ f* by setting √{square root over (ρ)}−1=η/√{square root over ( f*)} and solving for ρ by adding 1 and squaring. The resulting expression used in place of Δ_(η) is then a quadratic equation in η, for which its root provides means by which to express the relationship between rate drop and error exponent. Then ρ f* is (√{square root over ( f*)}+η)².

A twist here, is that in solving for the best f*, rather than starting from η=0, one may incorporate positive η in the optimization of

${{{{snr}\left( {3 + {{1/2}\; C}} \right)}\left( {\sqrt{{\overset{\_}{f}}^{*}} + \eta} \right)^{2}} - \frac{\log \left( {{\overset{\_}{f}}^{*}\sqrt{2\pi}\sqrt{2\; \log \; B}} \right)}{\log \; B}},$

for which, taking the derivative with respect to √{square root over ( f*)} and setting it to 0, a solution for this optimization is obtained as the root of a quadratic equation in √{square root over ( f*)}. Upon adding to that the other relevant terms of Δ_(second), namely ηsnr(1+1/2

)+h* one would have an explicit, albeit complicated, expression remaining in η.

Set Δ_(B)=Δ(snr, B) equal to Δ_(shape)+Δ_(m)+Δ_(f) at the above values of ζ, m, f. This Δ_(B) provides the rate drop envelope as a function only of snr and B. It corresponding to the large L regime in which one may take η to be small. Accordingly, Δ_(B) provides the boundary of the behavior by evaluating Δ with η=0.

The given values of m and f optimize Δ_(B), and the given ζ provides a tight bound, approximately optimizing the rate drop envelope Δ_(B). The associated total rate R_(tot) evaluated at these choices of parameters with η=0, denoted

_(m) is at least

(1−Δ_(B)). The associated bound on the fraction of mistakes of the inner code is δ_(mis)*=(snr/2

)(δ*− f).

Express the Δ_(η) bound as a strictly increasing function of the error exponent ε

${{\eta (ɛ)}{{snr}\left( {1 + {{1/2}\; C}} \right)}} + {ɛ\left( {3 + {{1/2}\; C}} \right)} + \frac{1 - {1/{\rho \left( {ɛ/\overset{\_}{f}} \right)}}}{\log \; B} + \sqrt{v\; {ɛ/\log}\; B}$

and let ε(Δ) denote its inverse for Δ≧0, [recognizing also per the statement of the Lemma above the cleaner upper bound dropping the 1/ρ(ε/ f)/log B term]. The part η(ε)snr/2

within the first term is from the contribution to 2δ_(mis) in the outer code rate. From the rate drop of the superposition inner code, the rest of Δ_(η) written as a function of ε is denoted Δ_(η,super) and let ε_(super)(Δ) denote its inverse function.

For a given total rate R_(tot)<

_(B), an associated error exponent ε is

ε((

_(B) −R _(tot))/

),

which is the evaluation of that inverse at (

_(B)−R_(tot))/

. Alternatively, in place of

_(B) its lower bound

(1−Δ_(B)) may be used and so take the error exponent to be ε(1−R_(tot)/

−Δ_(B)). Either choice provides an error exponent of a code of that specified total rate.

To arrange the constituents of this code, use the inner code mistake rate bound δ_(mis)=fac(δ*+ f+η(ε)), and set the inner code rate target R=R_(tot)/(1−δ_(mis)). Accordingly, for any number of sections L, set the codelength n, to be L log B/R rounded to an integer, so that the inner code rate L log Bin agrees with the target rate to within a factor of 1±1/n, and the total code rate (1−δ_(mis))R agrees with R_(tot) to within the same precision.

Theorem 38.

Rate and Reliability of the composite code: As a function of the section size B, let

_(B) and its lower bound

(1−Δ_(B)) be the rate envelopes given above, both near the capacity

for B large. Let a positive R_(tot)<

_(B) be given. If R_(tot)≦

(1−Δ_(B)), set the error exponent ε by

ε(1−Δ_(B) −R _(tot)/

).

Alternatively, to arrange the somewhat larger exponent, with η such that Δ_(η)=(

_(B)−R_(tot))/

, suppose that Δ_(η)≧δ_(mis); then set ε=ε_(η), that is, ε=ε((

_(B)−R_(tot))/

). To allow any R_(tot)<

_(B) without further condition, there is a unique η>0 such that Δ_(η,super)

=

_(B)/(1−δ_(mis)*)−R_(tot)/(1−δ_(mis)), at which set ε=ε_(η). In any of these three cases, for any number of sections L, the code consisting of a sparse superposition code and an outer Reed-Solomon code, having composite rate equal to R_(tot), to within the indicated precision, has probability of error not more than which is exponentially small in L near

κe ^(−L) ^(π) ^(ε),

which is exponetnially small in L_(π), near Lν/(2

), where κ==m(1+snr)^(1/2) B^(c)+2m is a polynomial in B with c=snr

, with number of steps m equal to the integer part of 1+snr log B.

Demonstration of Theorem 38 for Rate Assumption R_(tot)<

(1−Δ_(B)):

Set η>0 such that Δ_(η)=1−Δ_(B)−R_(tot)/

. Then the rate R_(tot) is expressed in the form

(1−Δ_(B)−Δ_(η)). In view of Lemma ?? and the development preceding it, this rate

(1−Δ)=

(1−Δ_(B)−Δ_(η)) is a lower bound on a rate of the established form (1−δ_(mis))

/(1−r/τ²), with parameter values that permit the decoder to be accumulative up to a point x* with shortfall δ*, providing a fraction of section mistakes not more than δ_(mis)=fac(δ*+η+ f), except in an event of the indicated probability with exponent ε_(h)=ε(Δ_(η)). This fraction of mistakes is corrected by the outer code. The probability of error bound from the earlier theorem herein is

+2me ^(−L) ^(η) ^(ε).

With m≦1+snr log B it is not more than the given κe^(−L) ^(η) ^(ε). The other part of the Theorem asserts a similar conclusion but with an improved exponent associated with arranging Δ_(η)=(

_(B)−R_(tot))/

, that is, R_(tot)=

_(B)(1−Δ_(η)). The inventors return to demonstrate that conclusion as a corollary of the next result.

One has the option to state the performance scaling results in terms of properties of the inner code. At any section size B, recognize that Δ_(B) above, at the η=0 limit, splits into a contribution from δ_(mis)*=(snr/2

)( f+δ*/(1+δ_(c))) and the rest which is a bound on the rate drop of the inner superposition code, which is denoted Δ_(super)*, in this small η limit. The rate envelope for such superposition codes is

${C_{super}^{*} = \frac{\left( {1 - {2\; {snr}\; \overset{\_}{f}}} \right)C}{\left( {1 + {{D\left( \delta_{c} \right)}/{snr}}} \right)\left\lbrack {\left( {1 + \delta_{a}} \right)^{2} + {r/\left( {2\; \log \; B} \right)}} \right\rbrack}},$

evaluated at f, δ_(a), δ_(c), r and ζ as specified above, with η=0, h=0 and ρ=1, again with a number of steps m equal to the integer part of 1+snr log B. It has

_(super)*≧

(1−Δ_(super)*).

Likewise recall that Δ_(η) splits into the part η(ε)snr/2

associated with δ_(mis) and the rest Δ_(η,super) expressed as a function of ε, for which ε_(super)(Δ) is its inverse.

Theorem 39.

Rate and Reliability of the Sparse Superposition Code: For any rate R<

_(super)*, let ε equal

ε_(super)(

_(super) *−R)/

).

Then for any number of sections L, the rate R sparse superposition code with adaptive successive decoder, makes a fraction of section mistakes less than δ_(mis)*+η(ε)snr/2

except in an event of probability less than κe^(−L) ^(π) ^(ε).

This conclusion about the sparse superposition code would also hold for values of the parameters other than those specified above, producing related tradeoffs between rate and the reliable fraction of section mistakes. The particular choices of these parameters made above is specific to the tradeoff that produces the best total rate of the composite code.

Demonstration of Theorem 39.

In view of the preceding analysis, what remains to establish is that the rate

_(super)*(1−Δ_(η,super))

is not more than the rate expression

$\frac{{C\left( {1 - h_{f}} \right)}\left( {1 - h^{*}} \right)}{\left( {1 + {{D\left( \delta_{c} \right)}/{snr}}} \right)\left( {1 + \delta_{a,\rho}} \right)^{2}\left( {1 + {r_{\eta}/\tau^{2}}} \right)}$

where Δ_(η,super) which is

ηsnr(1+r ₁/2 log B)+ε_(η)(3+1/C)(1+1/log B)+√{square root over (νε/log B)}

is at least

ηsnr(1+r ₁/τ²)+(log ρ)/log B+h*.

with ρ and h* satisfying the conditions of the Lemma, so that (once one accounts for the negligible remainder in 1/L), the indicated reliability holds with this rate. Here it is denoted that δ_(a,ρ)=δ_(a)+(log ρ)/2 log B to distinguish the value that occurs with ρ>1 with the value at ρ=0 used in the definition of

_(super)*. Likewise r_(η)/τ² is written for the expression r/τ²+ηsnr(1+r₁/τ²) to distinguish the value that occurs with η>0 with the value of r/τ² at η=0 used in the definition of

_(super)*. Factoring out terms in common, what is to be verified is that

$\frac{1 - \Delta_{\eta,{super}}}{\left( {1 + \delta_{a}} \right)^{2}\left( {1 + {r/\tau^{2}}} \right)}$

is not more than

$\frac{\left( {1 - h^{*}} \right)}{\left. \left( {1 + \delta_{a,\rho}} \right)^{2} \right)\left( {1 + {r_{\eta}/\tau^{2}}} \right\rbrack}.$

This is seen to be true by cross multiplying, rearranging, expanding the square in (1+δ_(a)+log ρ/2 log B)², using the lower bound on Δ_(η,super), and comparing term by term for the parts involving h* log ρ and η. This completes the demonstration of Theorem 39.

Next the rest of Theorem 38 is demonstrated, in view of what has been established. For the general rate condition R_(tot)<C_(B), for η≧0 the expression

${\Delta_{\eta,{super}}C} + \frac{R_{tot}}{1 - \delta_{mis}^{*} - {{snr}\; {\eta/2}\; C}}$

is a strictly increasing function of η in the interval [0, (2

/snr)(1−δ_(mis)*)) where the second term in this expression may be interpreted as the rate R of an inner code, with total rate R_(tot). This function starts at η=0 at the value R_(tot)/(1−δ_(mis)*) which is less than

_(B)/(1−δ_(mis)*) which is

_(super)*. So there is an η in this interval at which this function hits

_(super*. That is Δ) _(η,super)

+R=

_(super), or equivalently, Δ_(η,super)=(

_(super)*−R)/

. So Theorem 39 applies with exponent ε_(super)((

_(super)*−R)/

)).

Finally, to obtain the exponent ε((

_(B)−R_(tot))/

)), let Δ_(η)=

_(B)−R_(tot)/

. Examine the rate

_(B)(1−Δ_(η))

which is

(1−δ_(mis)*)

_(super)*(1−Δ_(η,super) −ηsnr/2

)

and determine whether it is not more than the following composite rate (obtained using the established inner code rate),

(1−δ_(mis) *−ηsnr/2

)

_(super)*(1−Δ_(ηsuper)).

These match to first order. Factoring out

_(super)* and canceling terms shared in common, the question reduces to whether −(1−δ_(mis)*) is not more than −(1−Δ_(η,super)), that is, whether δ_(mis)* is not more than Δ_(η,super), or equivalently, whether δ_(mis) is not more than Δ_(η), which is the condition assumed in the Theorem for this case. This completes the demonstration of Theorem 38.

11 Lower Bounds on Error Exponent:

The second line of the rate drop can be decomposed as

Δ_(m)+Δ _(f*)+Δ_(η,ρ),

where

$\Delta_{m} = {\frac{snr}{m - 1} + \frac{\log \; m}{\log \; B}}$

optimized at a number of steps 117, equal to an integer part of 2+snr log B. Further,

$\Delta_{{\overset{\_}{f}}^{*}} = {{\vartheta \; {\overset{\_}{f}}^{*}} - \frac{\log \left( {{\overset{\_}{f}}^{*}\sqrt{2\pi}\sqrt{2\; \log \; B}} \right)}{\log \; B}}$

where θ=snr(3+1/2

). The optimum value of f* equal to 1/[θ log B] and

Δ_(η,ρ)=ηθ₁+(ρ−1)/log B h.

Here θ₁=snr(1+1/2

).

The Δ_(η,ρ) is a strictly increasing function of the error exponent ε, where

Δ_(η,ρ)=θ₁η(ε)+(ρ−1)/log B+h.

Let R_(tot)≦

_(B) be given. The objective is to find the error exponent ε*=ε((

_(B)−R_(tot))/

), where ε solves the above equation with Δ_(η,ρ)=(

_(B)−R_(tot))/

. That is,

Δ_(η,ρ)=θ₁η(ε)+(ρ−1)/log B+h,

where ρ=ρ(ε/ f*).

Now ρ−1=(√{square root over (ρ)}−1)(√{square root over (ρ)}+1), which is (√{square root over (ρ)}−1)²+2(√{square root over (ρ)}−1). Correspondingly, using ε≧2 f*(√{square root over (ρ)}−1)², it follows that ε/2 f*+√{square root over (2ε/ f*)}≧ρ−1. Further, using η(ε)=√{square root over (ε/2)} and h*=√{square root over (νε/log B)}, one gets that

${\Delta_{\eta,\rho} \leq {{c_{1}ɛ} + {c_{2}\sqrt{ɛ}}}},{where}$ c₁ = ϑ/2 and $c_{2} = {\left\lbrack {\frac{\vartheta_{1}}{\sqrt{2}} + \sqrt{2\; {\vartheta/\log}\; B} + \sqrt{\frac{v}{\log \; B}}} \right\rbrack.}$

Solving the above quadratic in √{square root over (ε)} given above, one gets that

${ɛ \geq ɛ_{sol}} = {\left\lbrack \frac{{- c_{2}} + \sqrt{c_{2}^{2} + {4\Delta_{\eta,\rho}c_{1}}}}{2\; c_{1}} \right\rbrack^{2}.}$

It is sought what ε_(sol) looks like for Δ_(η,ρ)near 0. Noticing that ε_(sol) has the shape Δ_(η,ρ) ² for Δ_(η,ρ) near 0, it is desired to find the limit of ε_(sol)/Δ_(η,ρ) ² as Δ_(η,ρ) goes to zero. Using L′ Hospital's rule one get that this limiting value is 1/c₂ ². Correspondingly, using L_(π) is near Lν/2

, one gets that the error exponent is near

exp{−LΔ _(η,ρ) ²/ε₀},

for Δ_(η,ρ) near 0, where ξ₀=(2

/ν)c₂ ². This quantity behaves like snr²

for large snr and has the limiting value of (1+4/√{square root over (log B)})²/2 for snr tending to 0.

A simplified expression for ε_(sol) is now given. To simplify this, lower bound the function −a+√{square root over (a²+x)}, with x≧0 with a function of the form min{α√{square root over (x)},βx}. It is seen that

${{- a} + \sqrt{a^{2} + x}} \geq {\alpha \sqrt{x}\mspace{14mu} {for}\mspace{14mu} x} \geq \frac{4\alpha^{2}a^{2}}{\left( {1 - \alpha^{2}} \right)^{2}}$ ${{and} - a + \sqrt{a^{2} + x}} \geq {\beta \; x\mspace{14mu} {for}\mspace{14mu} x} \leq {\frac{1 - {2\beta \; a}}{\beta^{2}}.}$

Clearly, for the above to have any meaning one requires 0<α<1 and 0<β<½a. Further, it is seen that

$\begin{matrix} {{\min \left\{ {{\alpha \sqrt{x}},{\beta \; x}} \right\}} = {{\alpha \sqrt{x}\mspace{14mu} {for}\mspace{14mu} x} \geq \left( {\alpha/\beta} \right)^{2}}} \\ {= {{\beta \; x\mspace{14mu} {for}\mspace{14mu} x} \leq {\left( {\alpha/\beta} \right)^{2}.}}} \end{matrix}$

Correspondingly, equating (α/β)² with 4α²a²/(1−α²)², or equivalently equating (α/β)² with (1−2βa)/β², one gets that 1−α²=2aβ.

Now return to the problem of lower bounding ε_(sol). Set a=c₂ and x=4Δ_(η,ρ)c₁. Also particular choices of β and α are set to simplify the analysis. Take β=1/4a, for which α=1/√{square root over (2)}. Then the above gives that

$ɛ_{sol} \geq \frac{\left( {\min \left\{ {{\alpha \sqrt{4\Delta_{\eta,\rho}c_{1}}},{{\beta 4\Delta}_{\eta,\rho}c_{1}}} \right\}} \right)^{2}}{4\; c_{1}^{2}}$

which simplifies to

ε_(sol)≧min{Δ_(η,ρ)/2c ₁,Δ_(η,ρ) ²/4c ₂ ²}.

From Theorem 38, one get that the error probability is bounded by

κe ^(−L) ^(η) ^(ε) ^(sol) ,

which from the above, can also be bounded by the more simplified expression

κexp{−L _(η)min{Δ_(η,ρ)/2c ₁,Δ_(η,ρ) ²/4c ₂ ²}}.

It is desired to express this bound in the form,

κexp{−Lmin{Δ_(η,ρ)/ξ₁Δ_(η,ρ) ²)ξ_(2a}})

for some ξ₁, ξ₂. Using the fact that L_(π) is near Lν/2

, one gets that ξ₁ is (2

/ν)(2c₁), which gives

ξ₁=(1+snr)(6

+1).

One sees that ξ₁ goes to 1 as snr tends to zero. Further ξ₂=(2

/ν)4c₂ ², which behaves like 4C snr² for large snr. It has the limiting value of 2(1+4/√{square root over (log B)})² as snr tends to zero.

Improvement for Rates Near Capacity Using Bernstein Bounds:

The improved error bound associated with correct detection is given by

${\exp \left\{ {- \frac{\eta^{2}}{2\left( {V_{tot} + {\eta/\left( {3\; L_{\pi}} \right)}} \right)}} \right\}},$

where V_(tot)=V/L, with V≦{tilde over (c)}_(υ), where {tilde over (c)}_(υ)=(4

/ν²)(a₁+a₂/τ²)/τ. For small η, that is for rates near the rate envelope, the bound behaves like,

$\exp {\left\{ {{- L}\frac{\eta^{2}}{2\; V}} \right\}.}$

Consequently, for such η the exponent is,

$ɛ = {\frac{1}{d_{1}}{\frac{\eta^{2}}{2\; {\overset{\sim}{c}}_{v}}.}}$

Here d₁=L_(π)/L. This corresponds to η=√{square root over (d₂)}√{square root over (ε)}, where d₂=2d₁{tilde over (c)}_(υ). Here {tilde over (c)}_(υ)=(4

/ν²)(a₁/τ) and that d₁=ν/2

and τ≧√{square root over (2 log B)}, one gets that d₂≦1.62/ν√{square root over (log B)}. Substituting this upper bound for η in the expression for Δ_(η,ρ), it follows that

${\Delta_{\eta,\rho} \leq {{{\overset{\sim}{c}}_{1}ɛ} + {{\overset{\sim}{c}}_{2}\sqrt{ɛ}}}},{{{with}\mspace{14mu} {\overset{\sim}{c}}_{1}} = {\frac{\vartheta}{2}\mspace{14mu} {and}}}$ ${\overset{\sim}{c}}_{2} = {\left\lbrack {{\sqrt{d_{2}}\vartheta_{1}} + \sqrt{2{\vartheta/\log}\; B} + \sqrt{\frac{v}{\log \; B}}} \right\rbrack.}$

Consequently using the same reasoning as above one gets that using the Bernstein bound, for rates close to capacity, the error exponent is like

exp{−LΔ _(72,ρ) ²/{tilde over (ε)}₀},

for Δ_(η,ρ) near 0, where {tilde over (ε)}₀=(2

/ν){tilde over (c)}₂ ². This quantity behaves like 2d₂snr²

for large snr. Further, 2d₂ is near 3.24/√{square root over (log B)} for such snr. Notice now the error exponent is proportional to L√{square root over (log B)}Δ_(η,ρ) ², instead of the LΔ_(η,ρ) ² as before. We see that for B>36300, the quantity 3.24/√{square root over (log B)} is less than one producing a better exponent that before for rates near capacity and for larger snr than before.

12 Optimizing Parameters for Rate and Exponent for No Leveling Using the 1−xν Factor

From Corollary 20 one gets that

${GAP} = {\frac{r - r_{up}}{v\left( {\tau^{2} + r} \right)}.}$

Simplifying one gets

1+r/τ ²=(1+r _(up)/τ²)/(1−νGAP).

Recall that the rate assigned in the analysis herein of sparse superposition inner code is

$R = {\frac{\left( {1 - h^{\prime}} \right)C}{\left( {1 + \delta_{a}} \right)^{2}\left( {1 + \delta_{sum}^{2}} \right)\left( {1 + {r/\tau^{2}}} \right)}.}$

Here the terms involved in the leveling case are also included, even though it is the no leveling case being considering. This will be useful later on when generalizing to the case with the leveling. Further, with the Reed-Solomon outer code of rate 1−δ_(mis), which corrects the remaining fraction of mistakes, the total rate of the code is

$R_{tot} = {\frac{\left( {1 - \delta_{mis}} \right)\left( {1 - h^{\prime}} \right)C}{\left( {1 + \delta_{sum}^{2}} \right)\left( {1 + \delta_{a}} \right)^{2}\left( {1 + {r/\tau^{2}}} \right)}.}$

which using the above is equal to

$R_{tot} = {\frac{\left( {1 - \delta_{mis}} \right)\left( {1 - h^{\prime}} \right)\left( {1 - {v\; {GAP}}} \right)C}{\left( {1 + \delta_{sum}^{2}} \right)\left( {1 + \delta_{a}} \right)^{2}\left( {1 + {r_{up}/\tau^{2}}} \right)}.}$

Lemma 40.

Additive representation of rate drop: With a non-negative value for GAP less than 1/ν the rate R_(tot) is at least (1−Δ)

with Δ given by

$\Delta = {\frac{{snr}\; \delta^{*}}{\left( {1 + \delta_{c}} \right)2\; C} + \frac{r_{up}}{\tau^{2}} + \frac{D\left( \delta_{c} \right)}{snr} + {\frac{snr}{2\; C}\left( {\eta + \overset{\_}{f}} \right)} + {v\; {GAP}} + h^{\prime} + {2\delta_{a}} + {\frac{2\; C}{Lv}.}}$

Demonstration of Lemma 40:

Notice that

$\frac{\left( {1 - \delta_{mis}} \right)\left( {1 - h^{\prime}} \right)\left( {1 - {vGAP}} \right)}{\left( {1 + \delta_{sum}^{2}} \right)\left( {1 + \delta_{a}} \right)^{2}\left( {1 + {r_{up}\text{/}\tau^{2}}} \right)}$

is at least

$\frac{\left( {1 - h^{\prime}} \right)\left( {1 - {vGAP}} \right)}{\left( {1 + \delta_{sum}^{2}} \right)\left( {1 + \delta_{a}} \right)^{2}\left( {1 + {r_{up}\text{/}\tau^{2}}} \right)}$

minus δ_(mis)/(1+δ_(sum) ²). As before, the ratio δ_(mis)/(1+δ_(sum) ²) subtracted here is equal to

$\frac{snr}{2C}{\frac{\left( {\delta^{*} + \eta + \overset{\_}{f}} \right)}{\left( {1 - \delta_{c}} \right)}.}$

Further the first part of the difference is at least

1−h′−νGAP−δ _(sum) ²−2δ_(a) −r _(up)/τ².

Further using δ_(sum) ²≦D(δ_(c))/snr+2

/Lν one gets the result. This completes the demonstration of Lemma 40.

The second line of the rate drop is given by,

${{\frac{snr}{2C}\left( {{\eta \left( x^{*} \right)} + \overset{\_}{f}} \right)} + {v\; {GAP}} + h^{\prime} + {2\delta_{a}} + \frac{2C}{L\; v}},{where}$ η(x^(*)) = (1 − x^(*)υ)η^(std) and GAP = η^(std) + log  1/(1 − x^(*))/(m − 1).

Thus νGAP is equal to

νη^(std) +νc(x*)/(m−1),

where

c(x*)=log [1/(1−x*)].

Case 1:

h′=h+h_(f): Now optimize the second line of the rate drop when h′=h+h_(f). This leads to the following lemma.

Lemma 41.

Optimization of the second line of Δ. For any given positive η providing the exponent ε_(η) of the error probability, the values of the parameters 111, f* are specified to optimize their effect on the communication rate. The second line Δ_(second) of the total rate drop (

−R)/

bound Δ is the sum of three terms

Δ_(m)+Δ _(f*+Δ) _(η(x*)),

plus the negligible Δ_(L)=2

/(Lν)+(m−1)

/(L log B)+ε₃. Here

$\Delta_{m} = {{v\frac{c\left( x^{*} \right)}{m - 1}} + \frac{\log \; m}{\log \; B}}$

is optimized at a number of steps m equal to an integer part of 2+νc(x*)log B at which Δ_(m) is not more than

$\frac{1}{\log \; B} + \frac{\log \left( {2 + {v\; {c\left( x^{*} \right)}\log \; B}} \right)}{\log \; B}$

Likewise Δ _(f*) is given by

${{\vartheta \; {\overset{\_}{f}}^{*}} - \frac{\log \left( {{\overset{\_}{f}}^{*}\sqrt{2\pi}\sqrt{2\; \log \; B}} \right)}{\log \; B}},$

where θ=snr(2+1/2

). The above is optimized at the false alarm level f*=1/[θ log B] at which

$\Delta_{{\overset{\_}{f}}^{*}} = {\frac{1}{\log \; B} + {\frac{\log \left( {\vartheta {\sqrt{\log \; B}/\sqrt{4\pi}}} \right)}{\log \; B}.}}$

The Δη(x*) is given by

Δ_(η(x*))=η^(std)[ν+(1−x*ν)snr/2

]+(ρ−1)/log B+h

which is bounded by.

η^(std)θ₁+(ρ−1)/log B+h

where θ₁=ν+snr/2

.

Remark:

Since 1−x*=r/(snrτ²) and that r>r_(up), one has that 1−x*≧r_(up)/snrτ². Correspondingly, c(x*) is at most log(snr) log(τ²/r_(up)). The optimum number of steps can be bounded accordingly.

Demonstration:

Club all terms involving the number of steps Trl to get the expression for Δ_(m). It is then seen that optimization of Δ_(m) give the expression as in the proof statement.

Next, write

${2\delta_{a}} = {\frac{\log \left\lbrack {m\text{/}\left( {{\overset{\_}{f}}^{*}\sqrt{2\pi}\sqrt{2\log \; B}} \right)} \right\rbrack}{\log \; B}.}$

Further write f as (ρ−1) f*+ f* in terms involving f. For example h_(f)=2snr f is written as the sum of 2snr(ρ−1) f* plus 2snr f*. Now club all terms involving only f* (that is not (ρ−1) f*) into Δ _(f*). The result is Δ _(f*) to equal

${{\vartheta \; {\overset{\_}{f}}^{*}} - \frac{\log \left( {{\overset{\_}{f}}^{*}\sqrt{2\pi}\sqrt{2\; \log \; B}} \right)}{\log \; B}},$

optimized at f*=1/[θ log B], which determines a value of δ_(a) for the rate drop envelope independent of η.

The remaining terms are absorbed to give the expression for Δ_(η). Thus get that Δ_(η) is equal to

η^(std)[ν+(1−x*ν)/2

]+(ρ−1)/log B+h*.

The bound on Δ_(η(x*)) follows from using 1−x*ν≦1.

Error Exponent:

Here it is preferred to use Bernstein bounds for the error bounds associated with correct detection. Recall that that for rates near the rate envelope, that for η(x*) close to 0, the exponent is near

$ɛ = {\frac{1}{d_{1}}{\frac{\left( \eta^{std} \right)^{2}}{2{\overset{\sim}{c}}_{\upsilon}}.}}$

As before, this corresponds to η^(std)=√{square root over (d₂)}√{square root over (ε)}, where d₂=2d₁{tilde over (c)}_(υ). Substituting this upper bound for η^(std) in the expression for Δ_(η(x*)), one gets that

${\Delta_{\eta,\rho} \leq {{{\overset{\sim}{c}}_{1}ɛ} + {{\overset{\sim}{c}}_{2}\sqrt{ɛ}}}},{{{with}\mspace{14mu} {\overset{\sim}{c}}_{1}} = {\vartheta \text{/}2\mspace{14mu} {and}}}$ ${\overset{\sim}{c}}_{2} = {\left\lbrack {{\sqrt{d_{2}}\vartheta_{1}} + \sqrt{2{\vartheta/\log}\; B} + \sqrt{\frac{v}{\log \; B}}} \right\rbrack.}$

Consequently using the same reasoning as above one gets that using the Bernstein bound, for rates close to capacity, the error exponent is like

exp{−LΔ _(η,ρ) ²/{tilde over (ξ)}₀},

for Δ_(η,ρ) near 0, where {tilde over (ξ)}₀=(2

/ν){tilde over (c)}₂ ². For small snr, this quantity is near (√{square root over (d₂)}+√{square root over (2/log B)})². This quantity behaves like d₂snr²/2

for large snr. For large snr this is the same as what is obtained in the previous section.

Case 2:

1−h′=(1−h)^(m−1)/(1+h)^(m−1). It is easy to that this implies that h′≦2mh. The corresponding Lemma for optimization of rate drop for such h′ is presented.

Lemma 42.

Optimization of the second line of Δ. For any given positive η providing the exponent ε_(η) of the error probability, the values of the parameters m. f* are specified to optimize their effect on the communication rate. The second line Δ_(second) of the total rate drop (

−R)/

bound Δ is the sum of three terms

Δ_(m)+Δ _(f*)+Δ_(η(x*)),

plus the negligible Δ_(L)=2

/(Lν)+(m−1)

/(L log B)+ε₃. Here

$\Delta_{m} = {{v\frac{c\left( x^{*} \right)}{m - 1}} + \frac{\log \; m}{\log \; B}}$

is optimized at a number of steps m* equal to an integer part of 2+νc(x*)log B at which Δ_(m) is not more than

$\frac{1}{\log \; B} + \frac{\log \left( {2 + {v\; {c\left( x^{*} \right)}\log \; B}} \right)}{\log \; B}$

Likewise Δ _(f*) is given by

${{\vartheta \; {\overset{\_}{f}}^{*}} - \frac{\log \left( {{\overset{\_}{f}}^{*}\sqrt{2\pi}\sqrt{2\; \log \; B}} \right)}{\log \; B}},$

where θ=snr/2

. The above is optimized at the false alarm level f*=1/[θ log B] at which

$\Delta_{{\overset{\_}{f}}^{*}} = {\frac{1}{\log \; B} + {\frac{\log \left( {\vartheta {\sqrt{\log \; B}/\sqrt{4\pi}}} \right)}{\log \; B}.}}$

The Δ_(η(x*) is given by)

Δ_(η(x*))=η^(std)[ν+(1−x*ν)snr/2

]+(ρ−1)/log B+2m*h

which is bounded by,

η^(std)θ₁+(ρ−1)/log B+2m*h

where θ₁=ν+snr/2

.

Error Exponent:

Exactly similar to before, use Bernstein bounds for the correct detection error probabilities to get that,

Δ_(η,ρ) ≦{tilde over (c)} ₁ ε+{tilde over (c)} ₂√{square root over (ε)},

with {tilde over (c)}₁=θ/2 and

${\overset{\sim}{c}}_{2} = {\left\lbrack {{\sqrt{d_{2}}\vartheta_{1}} + \sqrt{2{\vartheta/\log}\; B} + {2m^{*}\sqrt{\frac{v}{\log \; B}}}} \right\rbrack.}$

Notice that since m*=2+νc(x*)log B, one has that

${\overset{\sim}{c}}_{2} = {\left\lbrack {{\sqrt{d_{2}}\vartheta_{1}} + \sqrt{2{\vartheta/\log}\; B} + {4\sqrt{\frac{v}{\log \; B}}} + {2{c\left( x^{*} \right)}v^{3/2}\sqrt{\log \; B}}} \right\rbrack.}$

As before the error exponent is like

exp{−LΔ _(η,ρ) ²/{tilde over (ξ)}₀},

for Δ_(η,ρ) near 0, where {tilde over (ξ)}=(2

/ν){tilde over (c)}₂ ².

Comparison of Envelope and Exponent for the Two Methods with and without Factoring 1−xν Term:

First concentrate attention on the envelope, which is given by

$\frac{1}{\log \; B} + \frac{m^{*}}{\log \; B} + \frac{1}{\log \; B} + {\frac{\log \left( {\vartheta {\sqrt{\log \; B}/\sqrt{4\pi}}} \right)}{\log \; B}.}$

For the first method, without factoring out the 1−xν term, θ=snr(3+1/2

) whereas for the second method (Case 2) it is snr/2

. The optimum number of steps m* is 2+snr log B for the first method and is 2+νc(x*) log B. Here c(x*) is log(snr) plus a term of order log log B. Correspondingly, the envelope is smaller for the second method.

Next, observe that the quantity c₂ determines the error exponent. The smaller the c₂, the better the exponent. For the first method it is

$\left\lbrack {\frac{\vartheta}{\sqrt{2}} + \sqrt{2{\vartheta/\log}\; B} + \sqrt{\frac{v}{\log \; B}}} \right\rbrack.$

where θ₁=snr(1+1/2

). Further θ is as given in the previous paragraph. For the second method it is given by

$\left\lbrack {\frac{\vartheta_{1}}{\sqrt{2}} + \sqrt{2{\vartheta/\log}\; B} + {2m^{*}\sqrt{\frac{v}{\log \; B}}}} \right\rbrack,$

where θ₁=ν+snr/2

. It is seen that for larger snr the latter is less producing a better exponent. To see this, notice that as a function of snr, the first term in c₂, i.e. θ₁/√{square root over (2)}, behaves like snr for the first case (without factorization of 1−xν) and is like snr/2

for the second case. The second term in c₂ is like √{square root over (snr)} for the first case and like

for the second. The third is near √{square root over (1/log B)} for the former case and behaves like log(snr) in the latter case. Consequently, it is the first term in c₂ which determines it behavior for larger snr for both cases. Since θ₁ is smaller in the second case, it is inferred that the second method is better for larger snr.

13 Composition with an Outer Code

Use Reed-Solomon (RS) codes (Reed and Solomon, SIAM 1960), as described for instance the book of Lin and Costello (2004), to correct any remaining mistakes from the adaptive successive decoder. The symbols for the RS code can be associated with that of a Galois field, say consisting of q elements and denoted by GF(q). Here q is typically taken to be of the form of a power of two, say 2^(m). Let K_(out), n_(out) be the message and blocklength respectively for the RS code. Further, if d_(RS) be the minimum distance between the codewords, then an RS code with symbols in GF(2^(m)) can have the following parameters:

n _(out)=2^(m)

n _(out) −K _(out) =d _(RS)−1

Here n_(out)−K_(out) gives the number of parity check symbols added to the message to form the codeword. In what follows it is convenient to take B to be equal to 2^(m) so that one can view each symbol in GF(2^(m)) as giving a number between 1 and B.

Now it is demonstrated how the RS code can be used as an outer code in conjunction with the inner superposition code, to achieve low block error probability. For simplicity assume that B is a power of 2. First consider the case when L equals B. Taking m=log₂ B, one sees that since L is equal to B, the RS codelength becomes L. Thus, one can view each symbol as representing an index specifying the selected term in each of the L sections. The number of input symbols is then K_(out)=L−d_(RS)+1, so setting δ=d_(RS)/L one sees that the outer rate R_(out)=K_(out)/n_(out), equals 1−δ+1/L which is at least 1−δ.

For code composition K_(out) log₂ B message bits become the K_(out) input symbols to the outer code. The symbols of the outer codeword, having length L, gives the labels of terms sent from each section using the inner superposition with codelength n=L log₂ B/R_(inner). From the received Y the estimated labels ĵ₁, ĵ₂, . . . ĵ_(L) using the adaptive successive decoder can be again thought of as output symbols for the RS codes. If {circumflex over (δ)}_(e) denotes the section mistake rate, it follows from the distance property of the outer code that if 2{circumflex over (δ)}_(e)≦δ then these errors can be corrected. The overall rate R_(comp) is seen to be equal to the product of rates R_(out)R_(inner) which is at least (1−δ)R_(inner). Since it is arranged for {circumflex over (δ)}_(e) to be smaller than some δ_(mis) with exponentially small probability, it follows from the above that composition with an outer code allows is to communicate with the same reliability, albeit with a slightly smaller rate given by (1−2δ_(mis))R_(inner).

The case when L<B can be dealt with by observing (as in Lin and Costello, page 240) that an (n_(out), K_(out)) RS code as above, can be shortened by length w, where 0≦w<K_(out), to form an (n_(out)−w, K_(out)−w) code with the same minimum distance d_(RS) as before. This is seen by viewing each codeword as being created by appending n_(out)−K_(out) parity check symbols to the end of the corresponding message string. Then the code formed by considering the set of codewords with the w leading symbols identical to zero has precisely the properties stated above.

With B equal to 2^(m) as before, the n_(out) is set to equal B, so taking w to be B−L we get an (n_(out)′, K_(out)′) code, with n_(out)′=L, K_(out)′=L−d_(RS)+1 and minimum distance d_(RS). Now since the codelength is L and the symbols of this code are in GF(B) the code composition can be carried out as before.

14 Appendix 14.1 Distribution of _(k,j)

Consider the general k>2 case. Focus on the sequence of coefficients

_(1,j),

_(2,j), . . . ,

_(k−1,j) ,V _(k,k,j) ,V _(k+1,k,j) , . . . ,V _(n,k,j)

used to represent X_(j) for j in J_(k−1) in the basis

$\frac{G_{1}}{G_{1}},\frac{G_{2}}{G_{2}},\ldots \;,\frac{G_{k - 1}}{G_{k - 1}},\xi_{k,k},\xi_{{k + 1},k},{\ldots \mspace{11mu} \xi_{n,k}},$

where the ξ_(i,k) for i from k to n are orthonormal vectors in

^(n), orthogonal to the G₁, G₂, . . . , G_(k−1). These are associated with the previously described representation X_(j)=Σ_(k′=1) ^(k−1)

_(k′,j)G_(k′)/∥G_(k′)∥+V_(k,j), except that here V_(k,j) is represented as Σ_(i=k) ^(n)V_(i,k,j)ξ_(i,k).

Let's prove that conditional on

, the distribution of the V_(i,k,j) is independent across i from k to n, and for each such i the joint distribution of (V_(i,k,j): jεJ_(k−1)) is Normal N_(j) _(k−1) (0,Σ_(k−1)). The proof is by induction in k. Along the way the conditional distribution properties of G_(k), Z_(k,j), and

_(k,j) are obtained as consequences. As for ŵ_(k) and δ_(k) the induction steps provide recursions which permit verification of the stated forms.

The V_(i,1,j)=X_(i,j) are independent standard normals.

To analyze the k=2 case, use the vectors U_(1,j)=U_(j) that arise in the first step properties in the proof of Lemma 1. There it is seen for unit vectors α, that the U_(jhu T)α for jεJ₁ have a joint N_(J) ₁ (0,Σ₁) distribution, independent of Y. When represented using the orthonormal basis Y/∥Y∥,ξ_(2,2), . . . , ξ_(n,2), the vector U_(j) has coefficients Z_(j)=U_(j) ^(T)Y/∥Y∥, and U_(j) ^(T)ξ_(2,2) through U_(j) ^(T)ξ_(n,2). Accordingly N_(j)=b_(1,j)Y/σ+U_(j) has representation in this basis with the same coefficients, except in the direction Y/∥Y∥ where Z_(j) is replaced by

_(j)=b_(1,j)∥Y∥/σ+Z_(j). The joint distribution of (V_(i,2,j)=U_(j) ^(T)ξ_(i,2): jεJ₁) is Normal N_(J) ₁ (0, Σ₁), independently for i=2 to n, and independent of ∥Y∥ and Z_(j): jεJ₁).

Proceed inductively for k≧2, presuming the stated conditional distribution property of the V_(i,k,j) to be true at k, conduct analysis to demonstrate its validity at k+1.

From the representation of V_(k,j) in the basis given above, the G_(k) has representation in the same basis as G_(i,k)=Σ_(jεdec) _(k−1) √{square root over (p_(j))}V_(i,k,j) for i from k to n. The coordinates less than k are 0, since the V_(k,j) and G_(k) are orthogonal to G₁, . . . , G_(k−1). The value of

_(k,j) is V_(k,j) ^(T)G_(k)/∥G_(k)∥, where the inner product (and norm) may be computed in the above basis from sums of products of coefficients for i from k to n.

For the conditional distribution of G_(i,k) given

_(k−1), independence across i, conditional normality and conditional mean 0 are properties inherited from the corresponding properties of the V_(i,k,j). To obtain the conditional variance of G_(i,k)=Σ_(jεdec) _(k−1) √{square root over (P_(j))}V_(i,k,j), use the conditional covariance Σ_(k−1)=I−δ_(k−1)δ_(k−1) ^(T) of V_(i,j,k) for j in J_(k−1). The identity part contributes Σ_(jεdec) _(k−1) P_(j) which is ({circumflex over (q)}_(k−1)+{circumflex over (f)}_(k−1))P; whereas, the δ_(k−1)δk−1 ^(T) part, using the presumed form of δ_(k−1), contributes an amount seen to equal ν_(k−1)[Σ_(jεsent∪dec) _(k−1) P_(j)/P]²P which is ν_(k−1){circumflex over (q)}_(k−1) ²P. It follows that the conditional expected square for the coefficients of G_(k) is

σ_(k) ² =[{circumflex over (q)} _(k−1) +{circumflex over (f)} _(k−1) −{circumflex over (q)} _(k−1) ²ν_(k−1) ]P.

Moreover, conditional on

_(k−1), the distribution of ∥G_(k)∥²=Σ_(i=k) ^(n)G_(i,k) ² is that of σ_(k) ²χ_(n−k+1) ², a multiple of a Chi-square with n−k+1 degrees of freedom.

Next represent V_(k,j)b_(k,j)G_(k)/σ_(k)+U_(k,j) using a value of b_(k,j) that follows an update rule (depending on

_(k−1)). It is represented using V_(i,k,j)=b_(k,j)G_(i,k)/σ_(k)+U_(i,k,j) for i from k to n, using the basis built from the ξ_(j,k).

The coefficient b_(k,j) is the value

[V_(i,k,j)G_(i,k)

_(k−1)]/σ_(k). Consider the product V_(i,k,j)G_(i,k) in the numerator. Use the representation of G_(i,k) as a sum of the √{square root over (P_(j′))}V_(i,k,j′) for j′εdec_(k−1). Accordingly, the numerator is Σ_(j′εdec) _(k−1) √{square root over (P_(j′))}[1_(j′=j)−δ_(k−1,j)δ_(k−1,j′)], which simplifies to √{square root over (P_(j))}[1_(jεdec) _(k−1) −ν_(k−1){circumflex over (q)}_(k−1)1_(j sent)]. So for j in J_(k)=J_(k−1)−dec_(k−1), there is the simplification

${b_{k,j} = {- \frac{{\hat{q}}_{k - 1}v_{k - 1}\beta_{j}}{\sigma_{k}}}},$

for which the product for j, j′ in J_(k) takes the form

${b_{k,j}b_{k,j^{\prime}}} = {\delta_{{k - 1},j}\delta_{{k - 1},j^{\prime}}{\frac{{\hat{q}}_{k - 1}v_{k - 1}}{1 + {{\hat{f}}_{k - 1}/{\hat{q}}_{k - 1}} - {{\hat{q}}_{k - 1}v_{k - 1}}}.}}$

Here the ratio simplifies to {circumflex over (q)}_(k−1) ^(adj)ν_(k−1)/(1−{circumflex over (q)}_(k−1) ^(adj)ν_(k−1)).

Now determine the features of the joint normal distribution of the U_(i,k,j)=V_(i,k,j)−b_(k,j)G_(i,k)/σ_(k) for jεJ_(k), given

_(k−1). These random variables are conditionally uncorrelated and hence conditionally independent given

_(k−1) across choices of i, but there is covariance across choices of j for fixed i. This conditional covariance

[U_(i,k,j)U_(i,k,j′)

_(k−1)] by the choice of b_(k,j) reduces to

[V_(i,j,k)V_(i,k,j′)

]−b_(k,j)b_(k,j′) which, for jεJ_(k), is 1_(j=1′)−δ_(k−1,j)δ_(k−1,j′)−b_(k,j)b_(k,j′). That is, for each i, the (U_(i,k,j): jεJ_(k)) have the joint N_(J) _(k) (0,Σ_(k)) distribution, conditional on

_(k−1), where Σ_(k) again takes the form 1_(j,j′)−δ_(k,j)δ_(k,j′) where

${{\delta_{k,j}\delta_{k,j^{\prime}}} = {\delta_{{k - 1},j}\delta_{{k - 1},j^{\prime}}\left\{ {1 + \frac{{\hat{q}}_{k - 1}^{adj}v_{k - 1}}{1 - {{\hat{q}}_{k - 1}^{adj}v_{k - 1}}}} \right\}}},$

for j,j′ now restricted to J_(k). The quantity in braces simplifies to 1/(1−{circumflex over (q)}_(k−1) ^(adj)νk⁻¹). Correspondingly, the recursive update rule for ν_(k) is

$v_{k} = {\frac{v_{k - 1}}{1 - {{\hat{q}}_{k - 1}^{adj}v_{k - 1}}}.}$

Consequently, the joint distribution for (Z_(k,j): jεJ_(k)) is determined, conditional on

_(k−1). It is also the normal N(0,Σ_(k)) distribution and (Z_(k,j): jεJ_(k)) is conditionally independent of the coefficients of G_(k), given

_(k−1). After all, the Z_(k,j)=U_(k,j) ^(T)G_(k)/∥G_(k)∥ have this N_(J) _(k) (0,Σ_(k)) distribution, conditional on G_(k) and

_(k−1), but since this distribution does not depend on G_(k) it yields the stated conditional independence.

Now

_(k,j)=X_(j) ^(T)G_(k)/∥G_(k)∥ reduces to V_(k,j) ^(T)G_(k)/∥G_(k)∥ by the orthogonality of the G₁ through G_(k−1) components of N_(j) with G_(k). So using the representation V_(k,j)=b_(k,j)G_(k)/σ_(k)+U_(k,j) one obtains

_(k,j) =b _(k,j) ∥G _(k)∥/σ_(k) +Z _(k,j).

This makes the conditional distribution of the

_(k,j), given

_(k−1), close to but not exactly normally distributed, rather it is a location mixture of normals with distribution of the shift of location determined by the Chi-square distribution of χ_(n−k+1) ²=∥G_(k)∥²/σ_(k) ². Using the form of b_(k,j), for j in J_(k), the location shift b_(k,j)χ_(n−k+1) may be written

−√{square root over (ŵ_(k) C _(j,R,B))}[χ_(n−k+1)/√{square root over (n)}]1_(j sent),

where ŵ_(k) equals nb_(k,j) ²/C_(j,R,B). The numerator and denominator has dependence on j through P_(j), so canceling the P_(j) produces a value for ŵ_(k). Indeed, C_(j,R,B)=(P_(j)/P)ν(L/R)log B equals n(P_(j)/P)ν and b_(k,j) ²=P_(j){circumflex over (q)}_(k−1) ^(adj)ν_(k−1) ²/[1−{circumflex over (q)}_(k−1) ^(adj)ν_(k−1)]. So this ŵ_(k) may be expressed as

${{\hat{w}}_{k} = {\frac{v_{k - 1}}{v}\frac{{\hat{q}}_{k - 1}^{adj}v_{k - 1}}{1 - {{\hat{q}}_{k - 1}^{adj}v_{k - 1}}}}},$

which, using the update rule for ν_(k−1), is seen to equal

${\hat{w}}_{k} = {\frac{v_{k - 1} - v_{k}}{v}.}$

Armed with G_(k), update the orthonormal basis of

^(n) used to represent X_(j), V_(k,j) and U_(k,j). From the previous step this basis was G₁/∥G₁∥, . . . , G_(k−1)/∥G_(k−1)∥ along with ξ_(k,k), ξ_(k+1,k), . . . , ξ_(n,k), where only the later are needed for the V_(k,j) and U_(k,j) as their coefficients in the directions G₁, G_(k−1) are 0.

Now Gram-Schmidt makes an updated orthonormal basis of

^(n), retaining the G₁/∥G₁∥, . . . , G_(k−1)/ but replacing ξ_(k,k), ξ_(k+1,k), . . . , 86 _(n,k) with G_(k)/∥G_(k)∥, ξ_(k+1,k+1), . . . , ξ_(n,k+1). By the Gram-Schmidt construction process, these vectors ξ_(i,k+1) for i from k+1 to n are determined from the original basis vectors (columns of the identity) along with the computed random vectors G₁, . . . , G_(k) and do not depend on any other random variables in this development.

The coefficients of U_(k,j) in this updated basis are U_(k,j) ^(T)G_(k)/∥G_(k)∥, U_(k,j) ^(T)ξ_(k+1,k+1), . . . , U_(k,j) ^(T)ξ_(n,k+1), which are denoted U_(k,k+1,j)=Z_(k,j) and U_(k+1,k+1,j), . . . , U_(k+1,n,j), respectively. Recalling the normal conditional distribution of the U_(k,j), these coefficients (U_(i,k+1,j): k≦i≦n, jεJ_(k)) are also normally distributed, conditional on

_(k−1) and G_(k), independent across i from k to n (this independence being a consequence of their uncorrelatedness, due to the orthogonality of the ξ_(i,k+1) and the independence of the coefficients U_(i,k,j) across i in the original basis); moreover, as seen already for i=k, for each i from k to n, the (U_(i,k+1,j): jεJ_(k)) inherit a joint normal N(0,Σ_(k)) conditional distribution from the conditional distribution that the (U_(i,k,j): jεJ_(k)) have. After all, these coefficients have this conditional distribution, conditioning on the basis vectors and

_(k−1), and this conditional distribution is the same for all such basis vectors. So, in fact, these (U_(i,k+1,j): k≦i≦n, jεJ_(k)) are conditionally independent of the G_(k) given

_(k−1).

Specializing the conditional distribution conclusion, by separating off the i=k case where the coefficients are Z_(k,j), one has that the (U_(i,k+1,j): k+1≦i≦n, jεJ_(k)) have the specified conditional distribution and are conditionally independent of G_(k) and (Z_(k,j): jεJ_(k)) given

_(k−1). It follows that the conditional distribution of (U_(i,k+1,j): k+1≦i≦n, jεJ_(k)) given

_(k)=(

_(k−1),∥G_(k)∥,Z_(k)) is identified. It is normal N(0, Σ_(k)) for each i, independently across i from k+1 to n, conditionally given

_(k).

Likewise, the vector V_(k,j)=b_(k,j)G_(k)/σ_(k)+U_(k,j) has representation in this updated basis with coefficient

_(k,j) in place of Z_(k,j) and with V_(i,k+1,j)=U_(i,k+1,j) for i from k+1 to n. So these coefficients (V_(i,k+1,j): k+1≦i≦n, jεJ_(k)) have the normal N(0, Σ_(k)) distribution for each i, independently across i from k+1 to n, conditionally given

_(k).

Thus the induction is established, verifying this conditional distribution property holds for all k=1, 2, . . . , n. Consequently, the Z_(k) and ∥G_(k)∥ have the claimed conditional distributions.

Finally, repeatedly apply ν_(k′)/ν_(k′−1)=1/(1−{circumflex over (q)}_(k′) ^(adj)ν_(k′−1)), for k′ from k to 2, each time substituting the required expression on the right and simplifying to obtain

$\frac{v_{k}}{v_{k - 1}} = {\frac{1 - {\left( {{\hat{q}}_{1}^{adj} + \ldots + {\hat{q}}_{k - 2}^{adj}} \right)v}}{1 - {\left( {{\hat{q}}_{1}^{adj} + \ldots + {\hat{q}}_{k - 2}^{adj} + {\hat{q}}_{k - 1}^{adj}} \right)v}}.}$

This yields ν_(k)=νŝ_(k), which, when plugged into the expressions for ŵ_(k), establishes the claims. The proof of Lemma 2 is complete.

14.2 The Method of Nearby Measures

Recall that the Renyi relative entropy of order α>1 (also known as the α divergence) of two probability measures

and

with density functions p(Z) and q(Z) for a random vector Z is given by

$\left. {{D_{\alpha}\left( {\mathbb{P}} \right.}Q} \right) = {\frac{1}{\alpha - 1}\log \mspace{14mu} {{_{Q}\left\lbrack \left( {{p(Z)}\text{/}{q(Z)}} \right)^{\alpha} \right\rbrack}.}}$

Its limit for large α is D_(∞()

_(∥)

_()=log∥p/q∥) _(∞).

Lemma 43.

Let

and

be a pair of probability measures with finite D_(α)(

∥

). For any event A, and α>1,

[A]≦[

.

If D_(α)(

∥

)≦c₀ for all α, then the following bound holds, taking the limit of large α,

[A]≦

[A]e^(c) ⁰ .

In this case the density ratio p(Z)/q(Z) is uniformly bounded by e⁰ ⁰ .

Demonstration of Lemma 43:

For convex f, as in Csiszar's f-divergence inequality, from Jensen's inequality applied to the decomposition of

[f(p(Z)/q(Z))] using the distributions conditional on A and its complement,

Af(

A/

A)+

A^(c) f(

A^(c)/

A^(c))≦

f(p(Z)/q(Z)).

Using in particular f(r)=r^(α) and throwing out the non-negative A^(c) part, yields

(

A)^(α)≦(

A)^(α−1)

[(p(Z)/q(Z))^(α)].

It is also seen as Holder's inequality applied to fq(p/q)1_(A). Taking the α root produces the stated inequality.

Lemma 44.

Let

_(Z) be the joint normal N(0,Σ) distribution, with Σ=I−bb^(T) where ∥b∥²=ν<1. Likewise, let

_(Z) be the distribution that makes the Z_(j) independent standard normal. Then the Rènyi divergence is bounded. Indeed, for all 1≦α≦∞.

D _(α)(

_(Z)∥

_(Z))≦c ₀.

where c₀=−(½)log [1−ν]. With ν=P(σ²−P), this constant is c₀=(½)log [1−P/σ²].

Demonstration of Lemma 44:

Direct evaluation of the a divergence between N(0,Σ) and N(0,I) reveals the value

$D_{\alpha} = {{{- \frac{1}{2}}\log {\Sigma }} - {\frac{1}{2\left( {\alpha - 1} \right)}\log {{{\alpha \; I} - {\left( {\alpha - 1} \right)\Sigma}}}}}$

Expressing Σ=I−Δ, it simplifies to

${\frac{1}{2}\log {{I - \Delta}}} - {\frac{1}{2\left( {\alpha - 1} \right)}\log {{I + {\left( {\alpha - 1} \right)\Delta}}}}$

The matrix Δ is equal to bb^(T), with b as previously specified with ∥b∥²=ν. The two matrices I−Δ and I+(α−1)Δ each take the form I+γbb^(T), with γ equal to −1 and (α−1) respectively.

The form I+γbb^(T) is readily seen to have one eigenvalue of 1+γν corresponding to an eigenvector b/∥b∥ and L−1 eigenvalues equal to 1 corresponding to eigenvectors orthogonal to the vector b. The log determinant is the sum of the logs of the eigenvalues, and so, in the present context, the log determinants arise exclusively from the one eigenvalue not equal to 1. This provides evaluation of D_(α) to be

${{{- \frac{1}{2}}{\log \left\lbrack {1 - v} \right\rbrack}} - {\frac{1}{2\left( {\alpha - 1} \right)}\log \left. {1 + {\left( {\alpha - 1} \right)v}} \right\rbrack}},$

where an upper bound is obtained by tossing the second term which is negative.

One sees that max_(Z)p(Z)/q(Z) is finite and equals [1/(1−ν)]^(1/2). Indeed, from the densities N(0, I−bb^(T)) and N(0, I) this claim can be established, noting after orthogonal transformation that these measures are only different in one variable, which is either N(0, 1−ν) or N(0, 1), for which the maximum ratio of the densities occurs at the origin and is simply the ratio of the normalizing constants. This completes the demonstration of Lemma 44.

With ν=P/(σ²+P) this limit −(½)log [1−ν] which is denoted as c₀ is the same as (½) log [1+P/σ²]. That it is the same as the capacity

appears to be coincidental.

Demonstration of Lemma 3:

The task is to show that for events A determined by

_(k) the probability

[A] is not more than

[A]e^(kc) ⁰ . Write the probability as an iterated expectation conditioning on

_(k−1). That is,

[A]=

A

_(k−1)]]. To determine membership in A, conditional on

_(k−1), one only needs Z_(k,J) _(k) =(Z_(k,j): jεJ_(k)) where J_(k) is determined

_(k−1). Thus

[A]=

[A]],

where the subscript on the outer expectation is used to denote that it is with respect to

and the subscripts on the inner conditional probability to indicate the relevant variables. For this inner probability switch to the nearby measure

ℚχ_(n − k + 1), Z_(k, J_(k))|ℱ_(k − 1).

These conditional measures agree concerning the distribution of the independent χ_(n−k+1) ², so the a relative entropy between them arises only from the normal distributions of the Z_(k,j) _(k) given

_(k−1). This α relative entropy is bounded by c₀.

To see this, recall that from Lemma 2 that

_(z) _(k,Jk |)

_(k−1) is N_(J) _(k) (0,Σ_(k)) with Σ_(k)=I−δ_(k)δ_(k) ^(T). Now

${\delta_{k}{^{2}{= {v_{k}{\sum\limits_{j \in {{sent}\bigcap\; J_{k}}}{P_{j}/P}}}}}}$

which is (1({circumflex over (q)}₁+ . . . +{circumflex over (q)}_(k−1)))ν_(k). Noting that ν_(k)={umlaut over (s)}_(k)ν and ŝ_(k)(1−({circumflex over (q)}₁+ . . . +{circumflex over (q)}_(k−1))) is at most 1, get that ∥δ_(k)∥²≦ν. Thus from Lemma 44, for all α≧1, the α relative entropy between

_(Z) _(k,Jk) _(|)

_(k−1) and the corresponding

conditional distribution is at most c₀.

So with the switch of conditional distribution, a bound is determined with a multiplicative factor of e^(c) ⁰ . The bound on the inner expectation is then a function of

_(k−1), so the conclusion follows by induction. This completes the demonstration of Lemma 3.

14.3 Demonstration of Lemmas on the Progress of q_(1,k)

Demonstration of Lemma 6:

Consider any step k with q_(1,k−1)−f_(1,k−1)≦x*. Now x=q_(1,k−1) ^(adj) is at least {tilde over (x)}=q_(1,k−1)−f_(1,k−1), where these are initialized to be 0 when k=1. Consider q_(1,k)=g_(L)(x)−η_(k) which is at least g_(L)({tilde over (x)})−η_(k), since the function g_(L) is increasing. By the gap property, it is at least {tilde over (x)}+gap({tilde over (x)})−η_(k), which in turn is at least q_(1,k−1)− f(x)+gap(x)−η(x), which is at least q_(1,k−1)+gap′.

The increase q_(1,k)−q_(1,k−1) is at least gap′ each such step, so the number of such steps m−1 is not more than 1/gap′. At the final step {tilde over (x)}=q_(1,m−1)−f_(1,m−1) exceeds x* so q_(1,m) is at least g_(L)(x*)−η_(m) which is 1−δ*−η_(m). This completes the demonstration of Lemma 6.

Demonstration of Lemma 5:

With a constant gap bound, the claim when f_(1,k)≦ f follows from the above, specializing f and η to be constant. As for the claim when f_(1,f)=kf, it is actually covered by the case that f_(1,k)≦ f, in view of the choice that f≦ f/m*. This completes the demonstration of Lemma 5.

14.4 The Gap has not More than One Oscillation

Demonstration of Lemma 21:

In the same manner as the derivative result for g_(num)(x), the g_(low)(x) has derivative with respect to x given by the following function, evaluated at z=z_(x),

$\left\{ {{\frac{\tau \; \Delta_{c}}{2}\left( {1 + \frac{z}{\tau}} \right)^{3}{\varphi (z)}} + {\int_{z}^{\infty}{\left( {1 + {t/\tau}} \right)^{2}{\varphi (t)}{t}}}} \right\} {\frac{R}{C^{\prime}}.}$

Subtracting 1+D(δ_(c))/snr from it gives the function der(z), which at z=z_(x) is the derivative with respect to x of G(z_(x))=g_(low)(x)−x−xD(δ_(c))/snr. The mapping from x to z_(x) is strictly increasing, so the sign of der(z) provides the direction of movement of either G(z) or of G(z_(x)).

Consider the behavior of der (z) for z≧−τ which includes [z₀, z₁]. At z=−τ the first term vanishes and the integral is not more than 1+1/τ², so under the stated condition on R, the der(z) starts out negative at z=−τ. Likewise note that der(z) is ultimately negative for large z since it approaches −(1+D(δ_(c))/snr. Let's see whether der(z) goes up anywhere to the right of −τ. Taking its derivative with respect to z, one obtains

$\begin{matrix} {{{der}^{\prime}(z)} = {\left\{ {{{- \frac{\tau \; \Delta_{c}}{2}}\left( {1 + {z/\tau}} \right)^{3}z\; {\varphi (z)}} + {\frac{3\Delta_{c}}{2}\left( {1 + {z/\tau}} \right)^{2}{\varphi (z)}} - {\left( {1 + {z/\tau}} \right)^{2}{\varphi (z)}}} \right\} {\frac{R}{C^{\prime}}.}}} & \; \end{matrix}$

The interpretation of der′(z) is that since der(z_(x)) is the first derivative of G(z_(x)), it follows that z_(x)′der′(z_(x)) is the second derivative, where z_(x)′ as determined in the proof of Corollary 13 is strictly positive for z>−τ. Thus the sign of the second derivative of the lower bound on the gap is determined by the sign of der′(z).

Factoring out the positive (1+z/τ)²φ(z)R/

for z>−τ, the sign of der′(z) is determined by the quadratic expression

−(τΔ_(c)/2)(1−z/τ)z+3Δ_(c)/2−1,

which has value 3Δ_(c)/2−1 at z=−τ and at z=0. The discriminant of whether there are any roots to this quadratic yielding der′(z)=0 is given by (τΔ_(c))²/4−2Δ_(c)(1−3Δ_(c)/2). Its positivity is determined by whether τ²Δ_(c)/4>2−3Δ_(c), that is, whether Δ_(c)>2/(τ²/4+3). If Δ_(c)≦2/(τ²/4+3) which is less than 2/3, then der′(z), which in that case starts out negative at z=−τ, never hits 0, so it stays negative for z≧−τ, so der(z) never goes up to the right of −τ and G(z) remains a decreasing function. In that decreasing case one may take z_(G)=z_(max)=−τ.

If Δ_(c)>2/(τ²/4+3), then by the quadratic formula there is an interval of values of z between the pair of points −τ/2±√{square root over (τ²/4−(2/Δ_(c))(1−3Δ/2))}{square root over (τ²/4−(2/Δ_(c))(1−3Δ/2))} within which der′(z) is positive, and within the associated interval of values of x the G(z_(x)) is convex in x. Outside of that interval there is concavity of G(z_(x)). So then either der(z) remains negative, so that G(z) is decreasing for z≧−τ, or there is a root z_(crit)>−τ where der(z) first hits 0 and der′(z)>0, i.e. that root, if there is one, is in this interval. Suppose there is such a root. Then from the behavior of der′(z) as a positive multiple of a quadratic with two zero crossings, the function G(z) experiences an oscillation.

Indeed, until that point z_(crit), the der(z) is negative so G(z) is decreasing. After that root, the der(z) is increasing between z_(crit) and z_(right), the right end of the above interval, so der(z) is positive and G(z) is increasing between those points as well. Now consider z≧z_(right), where der′(z)≦0, strictly so for z>z_(right). At z_(right) the der(z) is strictly positive (in fact maximal) and ultimately for large z the der(z) is negative, so for z>z_(right) the G(z) rises further until a point z=z_(max) where der(z)=0. To the right of that point since der′(z)<0, the der(z) stays negative and G(z) is decreasing. Thus der(z) is identified as having two roots z_(crit) and z_(max), and G(z) is unimodal to the right of z_(crit).

To determine the value of der(z) at z=0, evaluate the integral ∫_(z) ^(∞)(1−t/ν)²π(t)dt. In the same manner as in the preceding subsection, it is (1+1/τ²) Φ(z)+(2τ+z)φ(z)/τ².

Thus der(z) is

${\frac{R}{C^{\prime}}\left\{ {{\frac{\tau \; \Delta_{c}}{2}\left( {1 + {z/\tau}} \right)^{3}{\varphi (z)}} + {\frac{{2\tau} + z}{\tau^{2}}{\varphi (z)}} + {\left( {1 + {1/\tau^{2}}} \right)^{2}{\overset{\_}{\Phi}(z)}}} \right\}} - {\left( {1 + \frac{D\left( \delta_{c} \right)}{snr}} \right).}$

At z=0 it is

${\frac{R}{C^{\prime}}\left\{ {{\left( {\frac{\tau \; \Delta_{c}}{2} + \frac{2}{\tau}} \right)\frac{1}{\sqrt{2\pi}}} + {\left( {1 + \frac{1}{\tau^{2}}} \right)/2}} \right\}} - \left\lbrack {1 + {{D\left( {\delta_{c}/{snr}} \right\rbrack}.}} \right.$

It is non-negative if τΔ_(c)/(2√{square root over (2π)}) exceeds

[1+D(δ_(c))/snr]

/R−(1+1/τ²)/2−2/(τ√{square root over (2π)})

which using

/R=1+r/τ² is

$\frac{1}{2} + \frac{\left( {r - {1/2}} \right)}{\tau^{2}} + {\frac{D\left( \delta_{c} \right)}{snr}\left( {1 + {r/\tau^{2}}} \right)} - {2/{\left( {\tau \sqrt{2\pi}} \right).}}$

It is this expression which is called half for it tends to be not much more than ½. For instance, if D(δ_(c))/snr≦1/2 and (3/2)r≦(2/√{square root over (2π)})τ, then this expression is not more than 1−1/(2τ²) which is less than 1.

So then der(z) is non-negative at z=0 if

$\Delta_{c} \geq {\frac{2\sqrt{2\pi}\mspace{11mu} {half}}{\tau}.}$

Non-negativity of der(0) implies that the critical value of the function G satisfies z_(G)≦0.

Suppose on the other hand that der(0)<0. Then Δ_(c)<2√{square root over (2π)}half/τ, which is less than 2/3 when τ is at least 3√{square root over (2π)}half. Using the condition Δ_(c)≦2/3, the der′(z)<0 for z>0. It follows that G(z) is decreasing for z>0, and both z_(G) and z_(max) are non-positive.

Next consider the behavior of the function A(z), for which it is here shown that it too has at most one oscillation. Differentiating and collecting terms obtain that A′(z) is

A′(z)=2(1−Δ_(c))(z+τ)Φ(z)+Δ_(c)(z+τ)²φ(z).

Consider values of z in I_(τ)=(−τ,∞) to the right of −τ. Factoring out 2(z+τ), the sign behavior of A′(z) is determined by the function

M(z)=−(1−Δ_(c))Φ(z)+(Δ_(c)/2)(z+τ)φ(z).

This function M(z) is negative for large z as it converges to −2(1−Δ_(c)). Thus A(z) is decreasing for large z. At z=−τ the sign of M(z) is determined by whether Δ_(c)<1, if so then M(z) starts out negative, so then A(z) is initially decreasing, whereas in the unusual case of Δ_(c)≧1, the A(z) is initially increasing and so set z_(A)=−τ. Consider the derivative of M(z) given by

M′(z)=−[1−3Δ_(c)/2+(Δ_(c)/2)z(z+τ)]φ(z).

The expression in brackets is the same quadratic function of z considered above. It is centered and extremal at z_(cent)=−τ/2. This quadratic attains the value 0 only if Δ_(c) is at least Δ_(c)*=2/(τ²/4+3).

For Δ_(c)<Δ_(c)*, which is less than 1, the M′(z) stays negative and consequently M(z) is decreasing, so M(z) and A′(z) remains negative for z>−τ. Then A(z) is decreasing in I_(τ) (which actually implies the monotonicity of G(z) under the same condition on Δ_(c)).

For Δ_(c)≧Δ_(c)*, for which the function M′(z) does cross 0, this M′(z) is positive in the interval of values of z centered at z_(cent)=−τ/2 and heading up to the point z_(right) previously discussed. In this interval including [−τ/2, z_(right)] the function M(z) is increasing.

Let's see whether M(z) is positive, at or to the left of z_(eent). For Δ_(c)>1 that positivity already occurred at and just to the right of −τ. For Δ_(c)≦1, use the inequality Φ(z)≦φ(z)/(−z) for z<0. This lower bound is sufficient to demonstrate positivity in an interval of values of z centered at the same point z_(cent)=−τ/2, provided Δ_(c)τ²/4 is at least 2(1−Δ_(c)), that is, Δ_(c) at least Δ_(c)**=2/(τ²/4+2). Then z_(A) is not more than the left end of this interval, which is less than −τ/2. For Δ_(c)≧Δ_(c)**, this interval is where the same quadratic z(z+τ) is less than −2(1−Δ_(c))/Δ_(c). Then the M(z) is positive at −τ/2 and furthermore increasing from there up to z_(right), while, further to the right it is decreasing and ultimately negative. It follows that such M(z) has only one root to the right of −τ/2. The A′(z) inherits the same sign and root characteristics as M(z), so A(z) is unimodal to the right of −τ/2.

If Δ_(c) is between Δ_(c)* and Δ_(c)**, the lower bound invoked is insufficient to determine the precise conditions of positivity of M(z) at z_(cent), so resort in this case to the milder conclusion, from the negativity of M′(z) to the right of z_(right), that M(z) is decreasing there and hence it and A′(z) has at most one root to the right of that point, so A(z) is unimodal there. Being less than Δ_(c)**, the value of Δ_(c) is small enough that 2/Δ_(c)>τ²/4+2, and hence z_(right) is not more than [−τ+√{square root over (4)}]/2 which is −τ/2+1.

This completes the demonstration of Lemma 21.

It is remarked concerning G(z) that one can pin down down the location of z_(G) further. Under conditions on Δ_(c), it is near to and not more that a value near

${- \sqrt{2\; {\log \left( {\frac{1}{2\pi}\frac{{\tau\Delta}_{c}/2}{{{D\left( \delta_{c} \right)}/{snr}} + {\left( {r - 1} \right)/\tau^{2}}}} \right)}}},$

provided the argument of the logarithm is of a sufficient size. As said, precise knowledge of the value of z_(G) is not essential because the shape properties allow one to take advantage of the tight lower bounds on A(z) for negative z.

14.5 The Gap in the Constant Power Case

Demonstration of Corollary 19.

We are to show under the stated conditions that g(x)−x is smallest in [0,x*] at x=x*, when the power allocation is constant. For x in [0,1] the function z_(x) is one to one. In this u_(cut)=1 case, it is equal to z_(x)=[√{square root over ((1+r/τ²)/(1−xν))}{square root over ((1+r/τ²)/(1−xν))}−1]τ. It starts at x=0 with z₀ and at x=x* it is ζ. Note that (1+z_(o)/τ)²=1+r/τ². If r≧0 the z₀≧0, while, in any case, for r>−τT² the z₀ at least exceeds −τ. Invert the formula for z=z_(x) to express x in terms of z. Using g(x)=Φ(z) and subtracting the expression for x, we want the minimum of the function

${G(z)} = {{\Phi (z)} - {\frac{1}{v}{\left( {1 - \frac{\left( {1 + {r/\tau^{2}}} \right)}{\left( {1 + {z/\tau}} \right)^{2}}} \right).}}}$

Its value at z₀ is G(z₀)=Φ(z₀). Consider the minimization of G(z) for z₀≦z≦ζ, but take advantage, when it is helpful, of properties for all z>−τ. The first derivative is

${{\varphi (z)} - {\frac{2}{v\; \tau}\frac{\left( {1 + {r/\tau^{2}}} \right)}{\left( {1 + {z/\tau}} \right)^{3}}}},$

ultimately negative for very large z. This function has 0, 1, or 2 roots to the right of −τ. Indeed, to be zero it means that z solves

z ²−6 log(1+z/τ)=2 log(ντ/c)

where c=2(1+r/τ²)√{square root over (2π)}. The function on the left side υ(z)=z²−6 log(1+z/τ) is convex, with a value of 0 and a negative slope at z=0 and it grows without bound for large z. This function reaches its minimum value (lets call it υal<0) at a point z=z_(crit)>0, which solves 2z−6/(τ+z)=0, given by z_(crit)=(τ/2)[√{square root over (1+12/τ²)}−1] not more than 3/τ.

When υal>2 log(ντ/c) there are no roots, so G(z) is decreasing for z>−τ and has its minimum on [0, ζ] at z=ζ.

When 2 log(ντ/c) is positive (that is, when ντ>c, which is the condition stated in the corollary), it exceeds the value of the expression on the left at z=0, and G is increasing there. So from the indicated shape of the function υ(z), there is one root to the right of 0, which must be a maximizer of G(z), since G(z) is eventually decreasing. So then G(z) is unimodal for positive z and so if z₀≧0 its minimum in [z₀,ζ] is at either z=z₀ or z=ζ and this minimum is at least min{G(0), G(ζ)}. The value at z=0 is G(0)=½. So, with (r−r_(up))/[snrτT²+r₁)] less than ½, the minimum for z≧0 occurs at z=ζ, which demonstrates the first conclusion of Corollary 19.

If r is negative then z₀<0, and consider the shape of G(z) for negative z. Again with the assumption that 2 log(ντ/c) is positive, the function G(z) for z≧−τ is seen to have a minimizer at a negative z=z_(min) solving z²=2 log(ντ/c)−6 log(1+z/τ), where G′(z)=0, and G(z) is increasing between z_(min) and 0. It is inquired as to whether G(z) is increasing at z₀. If it is, then z₀≧z_(min) and G(z) is unimodal to the right of z₀. The value of the derivative there is

${{\varphi \left( z_{0} \right)} - \frac{2}{v\left( {\tau + z_{0}} \right)}},$

which is positive if

${z_{0}} \leq {\sqrt{2{\log \left( {{{v\left( {\tau + z_{0}} \right)}/2}\sqrt{2\pi}} \right)}}.}$

As shall be seen momentarily, z₀ is between r/τ and r/2τ, so this positive derivative condition is implied by

${r/\tau} \geq {- {\sqrt{2\mspace{11mu} {\log \left( {{{v\left( {\tau + {r/\tau}} \right)}/2}\sqrt{2\pi}} \right)}}.}}$

Then G(z) is unimodal to the right of z₀ and has minimum equal to min{G(z₀),G(ζ)}.

From the relationship (1+z₀/τ)²=1+r/τ², with −τ<z₀≦0, one finds that r=z₀(2τ+z₀), so it follows that z₀=r/(2τ+z₀) is between r/τ and r/2τ.

Lower bound G(z₀)=Φ(z₀) for z₀≦0 by the tangent line (½)+z₀φ(0), which is at least (½)+r/(τ√{square root over (2π)}). Thus when r is such that the positive derivative condition holds, there is the gap lower bound allowing r_(up)<r≦0 which is

min{½+r/(τ√{square root over (2π)}),(r−r _(up))/[snr(τ² +r ₁)]}.

This completes the demonstration of Corollary 19.

Next it is asked whether a useful bound might be available if G(z) is not increasing at this z₀≦0. Then z₀≦z_(min), and the minimum of G(z) in [z₀,ζ] is either at z_(min) or at ζ. The G(z) is

${\Phi (z)} + {\frac{\left( {1 + {z_{0}/\tau}} \right)^{2} - \left( {1 + {z/\tau}} \right)^{2}}{\left( {1 + {z/\tau}} \right)^{2}}.}$

Now since z_(min) is the negative solution to z²=2 log(ντ/c)−6 log(1+z/τ), it follows that there z_(min) is near −√{square root over (2 log(ντ/c))}. From the difference of squares, the second part of G(z_(min)) is near 2(z₀−z_(min))/τ which is negative. So for G(z_(min)) to be positive the Φ(z_(min)) would need to overcome that term. Now Φ(z_(min)) is near φ(z_(min))/|z_(min)|, and G′(z)=0 at z_(min) means that φ(z_(min)) equals the value (2/ντ)(1+z₀/τ)²/(1+z_(min)/τ)³. Accordingly, G(z_(min)) is near

$\frac{2\left( {1 + {z_{0}/\tau}} \right)^{2}}{v\; \tau \sqrt{2\mspace{11mu} {\log \left( {v\; {\tau/c}} \right)}}} + {\frac{2\left( {z_{0} + \sqrt{2\mspace{11mu} {\log \left( {v\; {\tau/c}} \right)}}} \right)}{\tau}.}$

The implication is that by choice of r one can not push z₀ much to the left of −√{square root over (2 log(ντ/c))} without losing positivity of G(z).

Next examine when r_(up) is negative, whether r arbitrarily close to r_(up) can satisfy the conditions. That would require the r_(p)/τ to be greater than −√{square root over (2π)}/2 and greater than

$- {\sqrt{2\mspace{11mu} {\log \left( {v\; {{\tau \left( {1 + {r_{up}/\tau^{2}}} \right)}/2}\sqrt{2\pi}} \right)}}.}$

However, in view of the formula for r_(up), it is near [1/(1+snr)−1]τ²=−ντ² when snr Φ(ζ) and ζ/τ are small. Consequently, r_(up)/τ is near −ντ. So if ντ is greater than a constant near √{square root over (2π)}/2 then the first of these conditions on r_(up)/τ is not satisfied. Also with this r_(up)/τ near −ντ the argument of the logarithm becomes ν(1−ν)τ/2√{square root over (2π)}, needed to be greater than 1. So if ντ is less than a constant near √{square root over (2π)}/2 then this argument of the logarithm is strictly less than 1. Thus the conditions for allowance of such negative r so close to r_(up) are vacuous. It is not possible to use an r so close to r_(up) when it is negative.

If when r_(up)/T is negative, near −ντ, try instead to have r/τ=−α√{square root over (2π)}/2 with 0≦α<1, then the first expression in the minimum becomes (1−α)/2, the second expression becomes r−r_(up)/[ν(τ+ζ)²] near 1+r/[ντ²] equal to 1−α√{square root over (2π)}/(2ντ), and the additional condition becomes

${\alpha {\sqrt{2\pi}/2}} \leq {\sqrt{2\mspace{11mu} {\log \left( {v\left( {\frac{\tau}{2\sqrt{2\pi}} - {\alpha/4}} \right)} \right)}}.}$

Which is acceptable with ντ at least a little more than 2√{square root over (2π)}e^(π/4). So in this way the 1+r/τ² factor becomes at best near 1√{square root over (2π)}/2τ. That is indeed a nice improvement factor in the rate, though not as ambitious as the unobtainable 1+r_(up)/τ² near 1−ν.

A particular negative r of interest would be one that makes (1+D(snr)/snr)(1+r/τ²)=1, for then even with constant power it would provide no rate drop from capacity. With this choice 1+r/τ²=1/(1+D(snr)/snr), the

${r/\tau} = {\frac{{- \tau}\; {{D({snr})}/{snr}}}{1 + {{D({snr})}/{snr}}}.}$

That a multiple of −τ, where the multiple is near snr/2 when snr is small. For G(z) to be increasing at the corresponding z₀, it is desired that the magnitude −r/τ be less than

$\sqrt{2\mspace{11mu} {\log \left( {v,{{{\tau \left( {1 + {r/\tau^{2}}} \right)}/2}\sqrt{2\pi}}} \right)}},$

where the ν(1+r/tau²)/2 may be expressed as a function of snr, and is also near snr/2 when snr is small. But that would mean that b=τsnr/2 is a value where b²≦2 log(b/√{square root over (2π)}, which a little calculus shows is not possible. Likewise, the above development of the case that z₀ is to the left, of z_(min), shows that one can not allow −r/τ to be much greater than the same value. 14.6 The Variance of Σ_(j sent)π_(j)1_(H) _(λ,k,j)

The variance of this weighted sum of Bernoulli that is to be to controlled is V/L=Σ_(j sent)π_(j) ²Φ(μ_(k,j)) Φ(μ_(k,j)) with μ_(k,j)=shift_(k,j)−τ. The shift_(k,j) may be written as √{square root over (c_(k)π_(j))}τ, where c_(k)=νL(1−h′)/(2R(1−xν)(1+δ_(a))²) evaluated at x=q_(1,k−1) ^(adj). Thus

${V/L} = {\sum\limits_{ell}\; {\pi_{()}^{2}\Phi {\overset{\_}{\Phi}\left( {\left( {\sqrt{c_{k}\pi_{()}} - 1} \right)\tau} \right)}}}$

where Φ Φ(z) is the function formed by the product Φ(z) Φ(z).

In the no-leveling (c=0) case π_((l)=e) ^(−2C(l−1)/L)2

/(νL) and c_(k)π_((l))=u_(l)R′/(R(1−xν)) with R′=

(1−h′)/(1+δ_(a))², where u_(l)=e^(−2C(l−1)/L).

With a quantifiably small error as here before, now replace the sum over the grid of values of t=l/L in [0,1] with the integral over this interval, yielding the value

$V = {\left( {2{\overset{\sim}{}/v}} \right)^{2}{\int_{0}^{1}{^{{- 4}\; t}\Phi {\overset{\_}{\Phi}\left( {\left( {\sqrt{\frac{^{{- 2}\; t}{^{\prime}/R}}{1 - {xv}}} - 1} \right)\tau} \right)}\ {{t}.}}}}$

Change variables to ũ=e^(−Ct) it is expressed as

$V = {\frac{\left( {2{\overset{\sim}{}/v}} \right)^{2}}{}{\int_{^{- }}^{1}{{\overset{\sim}{u}}^{3}\Phi {\overset{\_}{\Phi}\left( {\left( {{\overset{\sim}{u}\ \sqrt{\frac{R^{\prime}/R}{1 - {xv}}}} - 1} \right)\tau} \right)}{{\overset{\sim}{u}}.}}}}$

To upper bound it replace the ũ³ factor with 1 and change variables further to

$z = {\left( {{\overset{\sim}{u}\sqrt{\frac{R^{\prime}/R}{1 - {xv}}}} - 1} \right){\tau.}}$

Thereby obtain an upper bound and V of

$\frac{\sqrt{1 - {xv}}}{\tau \sqrt{R^{\prime}/R}}\frac{\left( {2{\overset{\sim}{}/v}} \right)^{2}}{}{\int{\Phi {\overset{\_}{\Phi}(z)}{{z}.}}}$

Now Φ Φ(z) has the upper bound (¼)e^(−z) ² ^(/2), which is √{square root over (2π)}φ(z)/4, which when integrated on the line yields

$V \leq {\frac{\sqrt{1 - {xv}}}{\tau \sqrt{^{\prime}/R}}\frac{\left( {\overset{\sim}{}/v} \right)^{2}}{}{\sqrt{2\pi}.}}$

When R≦R′, then using

≦

and x≦1, it yields

$V \leq {\frac{\sqrt{2\pi}}{v^{2}\tau}.}$

This provides the desired upper bound on the variance. 14.7 Slight Improvement to the Variance of Σ_(j sent) π_(j)1_(H) _(λ,k,j)

The variance of this weighted sum of Bernoulli that is desired to be controlled is V/L=Σ_(j sent) π_(j) ²Φ(μ_(k,j)) Φ(μ_(k,j)) with μ_(k,j)=shift_(k,j)−τ. The shift_(k,j) may be written as √{square root over (c_(k)π_(j))}τ, where c_(k)=νL(1−h′)/(2R(1−xν)(1+δ_(a))²) evaluated at x=q_(1,k−1) ^(adj). Thus

${V/L} = {\sum\limits_{ell}\; {\pi_{()}^{2}\Phi {\overset{\_}{\Phi}\left( {\left( {\sqrt{c_{k}\pi_{()}} - 1} \right)\tau} \right)}}}$

where Φ Φ(z) is the function formed by the product Φ(z) Φ(z).

In the no-leveling (c=0) case π_((l)=e) ^(−2C(l−1)/L)2

/(νL) and c_(k)π_((l))=u_(l)R′/(R(1−xν)) with R′=

(1−h′)/(1+δ_(a))², where u_(l)=e^(−2C(l−1)/L).

With a quantifiable small error as before, replace the sum over the grid of values of t=l/L in [0,1] with the integral over this interval, yielding the value

$V = {\left( {2{\overset{\sim}{}/v}} \right)^{2}{\int_{0}^{1}\ {^{{- 4}\; t}\Phi {\overset{\_}{\Phi}\left( {\left( {\sqrt{\frac{^{{- 2}\; t}{^{\prime}/R}}{1 - {xv}}} - 1} \right)\tau} \right)}{{t}.}}}}$

Changing variables to ũ=e^(−Ct) it is expressed as

$V = {\frac{\left( {2{\overset{\sim}{}/v}} \right)^{2}}{}{\int_{^{- }}^{1}\ {{\overset{\sim}{u}}^{3}\Phi {\overset{\_}{\Phi}\left( {\left( {{\overset{\sim}{u}\sqrt{\frac{R^{\prime}/R}{1 - {xv}}}} - 1} \right)\tau} \right)}{{\overset{\sim}{u}}.}}}}$

To upper bound the above expression, change variables further to

$z = {\left( {{\overset{\sim}{u}\sqrt{\frac{R^{\prime}/R}{1 - {xv}}}} - 1} \right){\tau.}}$

Thereby obtain an upper bound and V of

${\frac{\left( {1 - {xv}} \right)^{2}}{{\tau \left( {R^{\prime}/R} \right)}^{2}}\frac{\left( {2{\overset{\sim}{}/v}} \right)^{2}}{}{\int_{{({{^{- }c_{0}} - 1})}\tau}^{{({c_{0} - 1})}\tau}{\left( {1 + {z/\tau}} \right)^{3}\Phi {\overset{\_}{\Phi}(z)}\ {z}}}},$

where c₀=√{square root over (R′/R(1−xν))}. Now notice that (e^(−C)c₀−1)τ is at least −τ, making 1+z/τ≧0 on the interval of integration. Accordingly, the above integral is can be bounded from above by,

∫_(z≧−τ)(1+z/τ) ³Φ Φ(z)dz.

Further, the integral of (1+z/τ)³Φ Φ(z) for z≦−τ is a negligible term that is polynomially small in 1/B. Ignore that term in the rest of the analysis. Correspondingly, it is desired to bound the integral,

$\frac{\left( {1 - {xv}} \right)^{2}}{{\tau \left( {R^{\prime}/R} \right)}^{2}}\frac{\left( {2{\overset{\sim}{}/v}} \right)^{2}}{}{\int{\left( {1 + {z/\tau}} \right)^{3}\Phi {\overset{\_}{\Phi}(z)}\ {{z}.}}}$

Noticing that Φ Φ(z) is a symmetric function, the terms that are involve z and z³ after the expansion of (1+z/τ)³ above vanish upon integrating. Consequently, one only needs to bound the integral of Φ Φ(z) and z²Φ Φ(z). Doing this numerically the integral of the former is bounded by a₁=0.57 and that of the latter is bounded by a₂=0.48.

So ignoring the polynomially small term, the variance can be bounded by

$V \leq {\frac{\left( {1 - {xv}} \right)^{2}}{\left( {R^{\prime}/R} \right)^{2}}\frac{\left( {2{\overset{\sim}{}/v}} \right)^{2}}{\; \tau}{\left( {a_{1} + {a_{2}/\tau^{2}}} \right).}}$

which is less than,

$\left( {1 - {xv}} \right)^{2}\frac{\left( {4{/v^{2}}} \right)}{\tau}{\left( {a_{1} + {a_{2}/\tau^{2}}} \right).}$

Bound the above quantity by (4

/ν²)(a₁+a₂/τ²)/τ. Let's ignore the a₂/τ² term since this of smaller order. Then the variance can be bounded by 1.62/√{square root over (log B)}, where it is used that τ≧√{square root over (2 log B)} and that 4a₁/√{square root over (2)} is less than 1.62.

14.8 Normal Tails

Let Z be a standard normal random variable and let φ(z) be its probability density function, Φ(z) be its cumulative distribution and Φ(z)=1−Φ(z) be its upper tail probability for z>0. Here collect some properties of this probability, beginning with a conclusion from Feller. Most familiar is his bound Φ(z)≦(1/z)φ(z) which may be stated as φ(z)/ Φ(z) being at least z. His lower bound Φ(z)≧(1/z−1/z³)φ(z) has certain natural improvements, which are expressed through upper bounds on φ(z)/ Φ(z) showing how close it is to z.

Lemma 45.

For positive z the upper tail probability

{Z>z}= Φ(z) satisfies Φ(z)≦(√{square root over (2π)}/2)φ(z) and satisfies the Feller expansion

${{\overset{\_}{\Phi}(z)} \sim {{\varphi (z)}\left( {\frac{1}{z} - \frac{1}{z^{3}} + \frac{3}{z^{5}} - \frac{3.5}{z^{7}} + \ldots}\mspace{14mu} \right)}},$

with terms of alternating sign, where terminating with any term of positive sign produces an upper bound and terminating with any term of negative sign produces a lower bound. Furthermore, for z>0 the ratio φ(z)/ Φ(z) is increasing and is less than z+1/z. Further improved bounds are that it is less than ξ(z) equal to 2 for 0≦z≦1 and equal to z+1/z for z≧1, and, slightly better, φ(z)/ Φ(z) is less than [z+√{square root over (z²+4)}]/2. Moreover, the positive φ(z)/ Φ(z)−z is a decreasing function of z.

Demonstration of Lemma 45:

The expansion is from the book by Feller (1968, Vol. 1, Chap. VII), where it is noted in particular that the first order upper bound Φ(z)<(1/z)φ(z) is obtained from φ′(t)=−tφ(t) by noting that z Φ(z)=z∫_(z) ^(∞)φ(t)dt is less than ∫_(z) ^(∞)tφ(t)dt=φ(z). Thus the ratio φ(z)/Φ(z) exceeds z. It follows that the derivative of the ratio φ(z)/ Φ(z) which is [φ(z)/ Φ(z)−z]φ(z)/ Φ(z) is positive, so this ratio is increasing and at least its value at z=0, which is 2/√{square root over (2π)}.

Now for any positive c consider the positive integral ∫_(z) ^(∞)(t/c−1)²φ(t) dt. By expanding the square and using that (t²−1)φ(t) is the derivative of −tφ(t) on sees that this integral is (1+1/c²) Φ(z)−(2/c−z/c²)φ(z). Multiplying through by c², and assuming 2c>z, its positivity gives the family of bounds

${{\varphi (z)}/{\overset{\_}{\Phi}(z)}} \leq {\frac{c^{2} + 1}{{2c} - z}.}$

Evaluating it at c=z gives the upper bound on the ratio of (z²+1)/z=z+1/z. Note that since z/(z²+1) equals 1/z−1/[z(z²+1)] it improves on 1/z−1/z³ for all z≧0, Since φ(z)/ Φ(z) is increasing one can replace the upper bound z+1/z with its lower increasing envelope, which is the claimed bound ξ(z), noting that z+1/z takes its minimum value of 2 at z=1 and is increasing thereafter. For further improvement note that φ(z)/ Φ(z) equals a value not more than 1.53 at z=1, so the bound 2 for 0≦z≦1 may be replaced by 1.53.

Next let's determine the best bound of the above form by optimizing the choice of c. The derivative of the bound is the ratio of 2c(2c−z)−2(c²+1) and (2c−z)² and the c that sets it to 0 solves c²−zc−1=0 for which c=[z+√{square root over (z²+4)}]/2, and the above bound is then equal to this c.

As for the monotonicity of φ(z)/ Φ(z)−z, its derivative is (φ/ Φ)²−z(φ/ Φ)−1 which is a quadratic in the positive quantity φ/ Φ, abbreviating φ(z)/ Φ(z). Hence by inspecting the quadratic formula, this derivative is negative if φ/ Φ is less than or equal to [z+√{square root over (z²+4)}]/2, which it is by the above bound. This completes the demonstration of Lemma 45.

It is remarked that log φ(z)/ Φ(z) has first derivative φ(z)/ Φ(z)−z equal to the quantity studied in this lemma and second derivative found above to be negative. So the fact that φ(z)/ Φ(z)−z is decreasing is equivalent to the normal hazard function φ(z)/ Φ(z) being log-concave.

14.9 Tails for Weighted Bernoulli Sums

Lemma 46.

Let W_(j), 1≦j≦N be N independent Bernoulli(r_(j)) random variables. Furthermore, let α_(j), 1≦j≦K be non-negative weights that sum to 1 and let N_(α)=1/max_(j)α_(j). Then the weighted sum {circumflex over (r)}=Σ_(j)α_(j)W_(j) which has mean given by r*=Σ_(j)α_(j)r_(j), satisfies the following large deviation inequalities. For any r with 0<r<r*,

P({circumflex over (r)}<r)≦exp{−N _(α) D(r∥r*)}

and for any {tilde over (r)} with r*<{tilde over (r)}<1.

P({circumflex over (r)}>{tilde over (r)})≦exp{−N _(α) D({tilde over (r)}∥r*)}

where D(r∥r*) denotes the relative entropy between Bernoulli random variables of success parameters r and r*.

Demonstration of Lemma 46:

Let's prove the first part. The proof of the second part is similar.

Denote the event

$ = \left\{ {\underset{\_}{W}:{{\sum\limits_{j}{\alpha_{j}W_{j}}} \leq r}} \right\}$

with W denoting the N-vector of W_(j)'s. Proceeding as in Csiszar (Ann. Probab. 1984) it follows that

$\begin{matrix} {{P()} = {\exp \left\{ {- {D\left( {P_{\underset{\_}{W}|}{}P_{\underset{\_}{W}}} \right)}} \right\}}} \\ {\leq {\exp \left\{ {- {\sum\limits_{j}{D\left( {P_{W_{j}|}{}P_{W_{j}}} \right)}}} \right\}}} \end{matrix}$

Here

denotes the conditional distribution of the vector W conditional on the event

and

denotes the associated marginal distribution of W_(j) conditioned on

. Now

${\sum\limits_{j}{D\left( {P_{W_{j}|}{}P_{W_{j}}} \right)}} \geq {N_{\alpha}{\sum\limits_{j}{\alpha_{j}{{D\left( {P_{W_{j}|}{}P_{W_{j}}} \right)}.}}}}$

Furthermore, the convexity of the relative entropy implies that

${\sum\limits_{j}{\alpha_{j}{D\left( {P_{W_{j}|}{}P_{W_{j}}} \right)}}} \geq {{D\left( {\sum\limits_{j}{\alpha_{j}P_{W_{j}|}{}{\sum\limits_{j}{\alpha_{j}P_{W_{j}}}}}} \right)}.}$

The sums on the right denote α mixtures of distributions

and P_(W) _(j) , respectively, which are distributions on {0, 1}, and hence these mixtures are also distributions on {0, 1}. In particular, Σ_(j)α_(j)P_(W) _(j) is the Bernoulli(r*) distribution and Σ_(j)α_(j)

is the Bernoulli(r_(e)) distribution where

$r_{e} = {{E\left\lbrack {\sum\limits_{j}{\alpha_{j}W_{j}}} \middle|  \right\rbrack} = {{E\left\lbrack \hat{r} \middle|  \right\rbrack}.}}$

But in the event

it holds that {circumflex over (r)}≦r so it follows that r_(e)≦r. As r<r* this yields D(r_(e)∥r*)≧D(r∥r*). This completes the demonstration of Lenuna 46.

14.10 Lower Bounds on D

Lemma 47.

For p≧p* the relative entropy between Bernoulli(p) and Bernoulli(p*) distributions has the succession of lower bounds

${D_{Ber}\left( {p{}p^{*}} \right)} \geq {D_{Poi}\left( {p{}p^{*}} \right)} \geq {2\left( {\sqrt{p} - \sqrt{p^{*}}} \right)^{2}} \geq \frac{\left( {p - p^{*}} \right)^{2}}{2p}$

where D_(P) _(oi) (p∥p*)=p log p/p*+p*−p is also recognizable as the relative entropy between Poisson distributions of mean p and p* respectively.

Remark A:

There are analogous statements for pairs of probability distributions P and P* on a measurable space χ with densities p(x) and p*(x) with respect to a dominating measure μ. The relative entropy D(P∥P*) which is ∫p(x) log p(x)/p*(x)μ(dx) may be written as the integral of the non-negative integrand p(x) log p(x)/p*(x)+p*(x)−p(x), which exceeds (½)(p(x)−p*(x))²max{p(x),p*(x)}. It is familiar that D(P∥P*) exceeds the squared Bellinger distance H²(P,P*)=∫(√{square root over (p(x))}−√{square root over (p*(x))})²μ(dx). That fact arises for instance via Jensen's inequality, from which D exceeds 2 log 1/(1−(1/2)H²) which in turn is at least H².

Remark B:

When {circumflex over (p)} is the relative frequency of occurrence in N independent Bernoulli trials it has the bound P{{circumflex over (p)}>p}≦e^(−ND) ^(Ber) ^((p∥p*)) on the upper tail of the Binomial distribution of N{circumflex over (p)} for p>p*. In accordance with the Poisson interpretation of the lower bound on the exponent, one sees that this upper tail of the Binomial is in turn bounded by the corresponding large deviation expression that would hold if the random variables were Poisson.

Demonstration of Lemma 47:

The Bernoulli relative entropy may be expressed as the sum of two positive terms, one of which is p log p/p*+p*−p, and the other is the corresponding term with 1−p and 1−p* in place of p and p*, so this demonstrates the first inequality. Now suppose p>p*. Write p log p/p*+p*−p as p*F(s) where F(s)=2s² log s+1−s² with s²=p/p* which is at least 1. This function F and its first derivative F′(s)=4s log s have value equal to 0 at s=1, and its second derivative F″(s)=4+4 log s is at least 4 for s≧1. So by second order Taylor expansion F(s)≧2(s−1)² for s≧1. Thus p log p/p*+p*−p is at least 2(√{square root over (p)}−√{square root over (p*)})². Furthermore 2(s−1)²≧(s²−1)²/(2s²) as, taking the square root of both sides, it is seen to be equivalent to 2(s−1)≧s²−1, which, factoring out s−1 from both sides, is seen to hold for s≧1. From this the final lower bound (p−p*)²/(2p) is obtained. This completes the demonstration of Lemma 47.

ACKNOWLEDGMENT

The inventors herein thank Creighton Hauikulani who performed simulations of the decoder and earlier incarnations of it in fall 2009 and spring 2010 while completed masters studies in the Department of Statistics at Yale.

FINAL STATEMENT

Although the invention has been described in terms of specific embodiments and applications, persons skilled in the art may, in light of this teaching, generate additional embodiments without exceeding the scope or departing from the spirit of the invention described and claimed herein. Accordingly, it is to be understood that the drawing and description in this disclosure are proffered to facilitate comprehension of the invention, and should not be construed to limit the scope thereof. 

1. A sparse superposition encoder for a structured code for encoding digital information for transmission over a data channel, the encoder comprising: a memory for storing a design matrix formed of a plurality of column vectors X₁, X₂, . . . , X_(N), each such vector having n coordinates; and an input for entering a sequence of input bits u₁, u₂, . . . , u_(K) which determine a plurality of coefficients β₁, . . . , β_(N), each of the coefficients being associated with a respective one of the vectors of the design matrix to form codeword vectors, with real or complex-valued entries, in the form of superpositions β₁X₁+β₂X₂+ . . . +β_(N)X_(N), the sequence of bits u₁, u₂, . . . , u_(K) constituting at least a portion of the digital information.
 2. The encoder of claim 1, wherein the plurality of the coefficients β_(j) have selectably a determined non-zero value, or a zero value.
 3. The encoder of claim 1, wherein at least some of the plurality of the coefficients β_(j) have a predetermined value multiplied selectably by +1, or the predetermined value multiplied by −1.
 4. The encoder of claim 1, wherein at least some of the plurality of the coefficients β_(j) have a zero value, the number non-zero being denoted L and the value B=N/L controlling the extent of sparsity.
 5. The encoder of claim 1, wherein the design matrix stored in the memory is partitioned into L sections, with each section having B columns, where L>1.
 6. The encoder of claim 5, wherein each of the L sections of size B has B memory positions, one for each column of the dictionary, where B has a value corresponding to a power of 2, said positions addressed (selected) by binary strings of length log₂(B).
 7. The encoder of claim 6, wherein the input bit string of length K=L log₂B is split into L substrings, wherein for each section the associated substring provides the memory address of which one column is flagged to have a non-zero coefficient.
 8. The encoder of claim 5, wherein only 1 out of the B coefficients in each section is non-zero.
 9. The encoder of claim 5, wherein the L sections each has allocated a respective power that determines the squared magnitudes of the non-zero coefficients, denoted P₁, P₂, . . . , P_(L), one from each section.
 10. The encoder of claim 9, wherein the respectively allocated powers sum to a total P to achieve a predetermined transmission power.
 11. The encoder of claim 9, wherein the allocated powers are determined in a set of variable power assignments that permit a code rate up to value C_(B) where, with increasing sparsity B, this value approaches the capacity C=½ log₂(1+P/σ²) for the Gaussian noise channel of noise variance σ².
 12. The encoder of claim , wherein the code rate is R=K/n, for an arbitrary R where R<C, for an additive channel of capacity C, then the partitioned superposition code rate is R=(L log B)/n.
 13. The encoder of claim 4, wherein there is an adder that computes each entry of the codeword as the superposition of the corresponding dictionary elements for which the coefficients are non-zero.
 14. The encoder of claim 4, wherein there are n adders computing the codeword entries as the superposition of selected L columns of the dictionary in parallel.
 15. The encoder of claim 9 in which before initiating communications, the specified magnitudes are pre-multiplied to the columns of each section of the design matrix X, so that only adders are subsequently required of the encoder processor to form the code-words.
 16. The encoder of claim 15, wherein R/log(B) is arranged to be bounded so that encoder computation time to form the superposition of L columns is not larger than order n, yielding constant computation time per symbol sent.
 17. The encoder of claim 5, wherein the encoder size complexity is not more than the nBL memory positions to hold the design matrix and the n adders.
 18. The encoder of claim 5, wherein the value of B is chosen to be not more than a constant times n, whereupon also L is not more than n divided by a log, so that the encoder size complexity nBL is not more than n³.
 19. The encoder of claim 16, where in R/log(B) is chosen to be small.
 20. The encoder of claim 6, wherein the input to the code arises as the output of a Reed-Solomon outer code of alphabet size B and length L for the purpose of maintaining an optimal separation, with distance measured by the fraction of distinct selections of non-zero terms.
 21. The encoder of claim 1 in which the dictionary is generated by independent standard normal random variables.
 22. The encoder of claim 21 in which the random variables are provided to specified predetermined precision.
 23. The encoder of claim 1 in which the dictionary is generated by independent, equiprobable, +1 or −1, random variables. 24-76. (canceled) 